Remove unnecessary CUDA utilities (#4855)

This PR removes some utilities that were updated in #4830 but are no longer needed.

xref: rapidsai/build-planning#117

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

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

URL: #4855

benchmarks/cugraph/pytest-based/bench_algos.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -42,7 +42,6 @@ def setFixtureParamNames(*args, **kwargs):
 from cugraph.structure.number_map import NumberMap
 from cugraph.generators import rmat
 from cugraph.testing import utils, mg_utils
-from cugraph.utilities.utils import is_device_version_less_than
 
 from cugraph_benchmarking.params import (
     directed_datasets,
@@ -362,9 +361,6 @@ def bench_sorensen(gpubenchmark, unweighted_graph):
     gpubenchmark(sorensen, G, vert_pairs)
 
 
-@pytest.mark.skipif(
-    is_device_version_less_than((7, 0)), reason="Not supported on Pascal"
-)
 def bench_louvain(gpubenchmark, graph):
     louvain = dask_cugraph.louvain if is_graph_distributed(graph) else cugraph.louvain
     gpubenchmark(louvain, graph)

ci/notebook_list.py

@@ -41,16 +41,7 @@ def _get_cuda_version_string():
     minor //= 10
     return f"{major}.{minor}"
 
-
-def _is_ampere_or_newer():
-    status, device_id = runtime.cudaGetDevice()
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device.")
-    status, device_prop = runtime.cudaGetDeviceProperties(device_id)
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device properties.")
-    return (device_prop.major, device_prop.minor) >= (8, 0)
-
+cuda_version_string = _get_cuda_version_string()
 
 parser = argparse.ArgumentParser(description="Condition for running the notebook tests")
 parser.add_argument("runtype", type=str)
@@ -86,10 +77,6 @@ def _is_ampere_or_newer():
                 )
                 skip = True
                 break
-            elif _is_ampere_or_newer() and re.search("# Does not run on Ampere", line):
-                print(f"SKIPPING {filename} (does not run on Ampere)", file=sys.stderr)
-                skip = True
-                break
             elif re.search("# Does not run on CUDA ", line) and (
                 cuda_version_string in line
             ):

python/cugraph/cugraph/traversal/ms_bfs.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2025, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -56,7 +56,6 @@ def _get_feasibility(G, sources, components=None, depth_limit=None):
 
     # Fixme not implemented in RMM yet
     # using 96GB upper bound for now
-    # mem = get_device_memory_info()
     mem = 9.6e10
     n_sources = sources.size
     V = G.number_of_vertices()

python/cugraph/cugraph/utilities/utils.py

@@ -18,8 +18,6 @@
 import cudf
 from cudf.core.column import as_column
 
-from cuda.bindings import runtime
-
 from warnings import warn
 
 # optional dependencies
@@ -207,44 +205,6 @@ def get_traversed_path_list(df, id):
     return answer
 
 
-def is_cuda_version_less_than(min_version):
-    """
-    Returns True if the version of CUDA being used is less than min_version
-    """
-    status, version = runtime.getLocalRuntimeVersion()
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA runtime version.")
-    major = version // 1000
-    minor = (version % 1000) // 10
-    return (major, minor) < min_version
-
-
-def is_device_version_less_than(min_version):
-    """
-    Returns True if the version of CUDA being used is less than min_version
-    """
-    status, device_id = runtime.cudaGetDevice()
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device.")
-    status, device_prop = runtime.cudaGetDeviceProperties(device_id)
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device properties.")
-    return (device_prop.major, device_prop.minor) < min_version
-
-
-def get_device_memory_info():
-    """
-    Returns the total amount of global memory on the device in bytes
-    """
-    status, device_id = runtime.cudaGetDevice()
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device.")
-    status, device_prop = runtime.cudaGetDeviceProperties(device_id)
-    if status != runtime.cudaError_t.cudaSuccess:
-        raise RuntimeError("Could not get CUDA device properties.")
-    return device_prop.totalGlobalMem
-
-
 # FIXME: if G is a Nx type, the weight attribute is assumed to be "weight", if
 # set. An additional optional parameter for the weight attr name when accepting
 # Nx graphs may be needed.  From the Nx docs: