[gsoc24] Add an initial batch of proposals for compiler-research.org

_gsocorgs/2024/compres.md

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+---
+title: "Compiler Research"
+author: "Vassil Vassilev"
+layout: default
+organization: CompRes
+logo: CompRes-logo.png
+description: |
+  The Compiler Research Group is a group of programming language enthusiasts at Princeton University and CERN. It's primary goal is to research foundational software tools that help scientists program for speed, interoperability, interactivity, flexibility, and reproducibility.
+---
+
+{% include gsoc_proposal.ext %}

_gsocprojects/2024/project_Clad.md

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+---
+project: Clad
+layout: default
+logo: Clad-logo.png
+description: |
+  [Clad](https://clad.readthedocs.io/en/latest/) enables 
+  automatic differentiation (AD) for C++. It is based on LLVM compiler 
+  infrastructure and is a plugin for Clang compiler. Clad is based on 
+  source code transformation. Given C++ source code of a mathematical 
+  function, it can automatically generate C++ code for computing derivatives
+  of the function.
+summary: |
+  Clad is an automatic differentiation (AD) tool for C++
+---
+
+{% include gsoc_project.ext %}
+

_gsocproposals/2024/proposal_Clad-GPUReverseMode.md

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+---
+title: Enable reverse-mode automatic differentiation of (CUDA) GPU kernels using Clad
+layout: gsoc_proposal
+project: Clad
+year: 2023
+difficulty: medium
+duration: 350
+mentor_avail: June-October
+organization:
+  - CompRes
+---
+
+## Description
+
+Clad is an automatic differentiation (AD) clang plugin for C++. Given a C++ source code of a mathematical function, it can automatically generate C++ code for computing derivatives of the function. Clad has found uses in statistical analysis and uncertainty assessment applications. In scientific computing and machine learning, GPU multiprocessing can provide a significant boost in performance and scalability. This project focuses on enabling the automatic differentiation of CUDA GPU kernels using Clad. This will allow users to take advantage of the power of GPUs while benefiting from the accuracy and speed of automatic differentiation.
+
+## Project Milestones
+
+* Research about automatic differentiation of code involving CUDA GPU kernels. Prepare a report and an initial strategy to follow.This may involve brainstorming and the need for innovative solutions. 
+* Enable reverse-mode automatic differentiation of CUDA GPU kernels and calls to CUDA GPU kernels from the host code.
+* Add proper tests and documentation.
+
+## Requirements
+
+* Automatic differentiation
+* CUDA C++ programming
+* C++ programming and Clang frontend
+
+## Mentors
+* **[Parth Arora](mailto:[email protected])**
+* [Vassil Vassilev](mailto:[email protected])
+
+## Links
+* [Repo](https://github.com/vgvassilev/clad)

_gsocproposals/2024/proposal_Clad-ObjectOrientedAD.md

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+---
+title: Add support for consteval and constexpr functions in Clad
+layout: gsoc_proposal
+project: Clad
+year: 2023
+difficulty: medium
+duration: 350
+mentor_avail: June-October
+organization:
+  - CompRes
+---
+
+## Description
+
+Clad is an automatic differentiation (AD) clang plugin for C++. Given a C++ source code of a mathematical function, it can automatically generate C++ code for computing derivatives of the function. Clad has found uses in statistical analysis and uncertainty assessment applications.
+
+Object oriented paradigms (OOP) provide a structured approach for complex use cases, allowing for modular components that can be reused & extended. OOP also allows for abstraction which makes code easier to reason about & maintain. Gaining full OOP support is an open research area for automatic differentiation codes.
+
+This project focuses on improving support for differentiating object-oriented constructs in Clad. This will allow users to seamlessly compute derivatives to the algorithms in their projects which use an object-oriented model. C++ object-oriented constructs include but are not limited to: classes, inheritance, polymorphism, and related features such as operator overloading.
+
+
+## Project Milestones
+
+* Study the current object-oriented differentiable programming support in Clad. Prepare a report of missing constructs that should be added to support the automatic differentiation of object-oriented paradigms in both the forward mode AD and the reverse mode AD.
+* Some of the missing constructs are: differentiation of constructors, limited support for differentiation of operator overloads, reference class members, and no way of specifying custom derivatives for constructors.
+* Add support for the missing constructs.
+* Add proper tests and documentation.
+
+
+## Requirements
+
+* Automatic differentiation
+* C++ programming
+* Clang frontend
+
+## Mentors
+* **[Parth Arora](mailto:[email protected])**
+* [Vassil Vassilev](mailto:[email protected])
+
+## Links
+* [Repo](https://github.com/vgvassilev/clad)

_gsocproposals/2024/proposal_Clad-constant-evaluation-contexts.md

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+---
+title: Add support for consteval and constexpr functions in Clad
+layout: gsoc_proposal
+project: Clad
+year: 2023
+difficulty: medium
+duration: 350
+mentor_avail: June-October
+organization:
+  - CompRes
+---
+
+## Description
+
+In mathematics and computer algebra, automatic differentiation (AD) is a set of
+techniques to numerically evaluate the derivative of a function specified by a
+computer program. Automatic differentiation is an alternative technique to
+Symbolic differentiation and Numerical differentiation (the method of finite
+differences).  Clad is based on Clang which provides the necessary facilities
+for code transformation.  The AD library can differentiate non-trivial
+functions, to find a partial derivative for trivial cases and has good unit test
+coverage.
+
+C++ provides the specifiers consteval and constexpr to allow compile time
+evaluation of functions. `constexpr` declares a possibility, i.e the function
+will be evaluated at compile time if possible, else at runtime; whereas
+`consteval` makes it mandatory, i.e every call to the function must produce a
+compile-time constant.
+
+The aim of this project is to ensure that same semantics are followed by the
+generated derivative function, i.e if the primal function is evaluated at
+compile time (because of constexpr or consteval specifier), then the generated
+derivative code should also have the same specifier to be evaluatable at compile
+time.
+
+This will enable clad to demonstrate the benefits of doing automatic
+differentiation directly on C++ frontend to utilize the benefits of clang's
+infrastructure.
+
+After successful completion of the project the code snippet should work as
+expected:
+
+```cpp
+    #include <cstdio>
+    #include "clad/Differentiator/Differentiator.h"
+
+    constexpr double sq(double x) { return x*x; }
+    consteval double fn(double x, double y, double z) {
+      double res = sq(x) + sq(y) + sq(z);
+      return res;
+    }
+
+    int main() {
+      auto d_fn = clad::gradient(fn);
+      double dx = 0, dy = 0, dz = 0;
+      d_fn.execute(3, 4, 5, &dx, &dy, &dz);
+      printf("Gradient vector: [%.2f, %.2f, %.2f]", dx, dy, dz);
+      return 0;
+    }
+```
+
+
+## Project Milestones
+
+* Add support for differentiation with respect to consteval and constexpr
+  functions in the forward mode.
+* Add support for differentiation with respect to consteval and constexpr
+  functions in the reverse mode.
+* Extend the unit test coverage.
+* Develop tutorials and documentation.
+* Present the work at the relevant meetings and conferences.
+
+
+## Requirements
+
+* Automatic differentiation
+* C++ programming
+* Clang frontend
+
+## Mentors
+* **[Vaibhav Thakkar](mailto:[email protected])**
+* [Vassil Vassilev](mailto:[email protected])
+
+## Links
+* [Repo](https://github.com/vgvassilev/clad)

gsoc/2024/mentors.md

@@ -6,6 +6,11 @@ layout: plain
 **Note for contributors:** entries must be sorted in **last name** alphabetic order
 
 ## Full Mentor List (Name, Email, Org)
+* Parth Arora [[email protected]](mailto:[email protected]) CompRes
+* Jakob Blomer [[email protected]](mailto:[email protected]) CERN
 * Benedikt Hegner [[email protected]](mailto:[email protected]) CERN
+* Wim Lavrijsen [[email protected]](mailto:[email protected]) CompRes
+* Alexander Penev [[email protected]](mailto:[email protected]) CompRes
+* Vaibhav Thakkar [[email protected]](mailto:[email protected]) CompRes
+* Vassil Vassilev [[email protected]](mailto:[email protected]) CompRes
 * Valentin Volkl [[email protected]](mailto:[email protected]) CERN
-* Jakob Blomer [[email protected]](mailto:[email protected]) CERN