Add project for Clad and Kokkos

project_metadata.yml

@@ -1,6 +1,7 @@
 categories:
   - Analysis tools
   - Generators
+  - HPC
   - Simulation
   - Open science
   - ML/AI

projects/clad_kokkos.yml

@@ -0,0 +1,64 @@
+---
+name: Implement Differentiating of the Kokkos Framework With Clad
+postdate: 2024-04-11
+categories:
+  - ML/AI
+  - HPC
+durations:
+  - 3 months
+skillset:
+  - C++
+experiments:
+  - Any
+project:
+  - compiler research
+location:
+  - Remote
+program:
+  - Any
+commitment:
+  - Any
+status:
+  - Available
+shortdescription: Implement Differentiating of the Kokkos Framework With Clad
+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.
+
+  The Kokkos C++ Performance Portability Ecosystem is a production level solution
+  for writing modern C++ applications in a hardware agnostic way. It is part of
+  the US Department of Energies Exascale Project – the leading effort in the US
+  to prepare the HPC community for the next generation of super computing
+  platforms. The Ecosystem consists of multiple libraries addressing the primary
+  concerns for developing and maintaining applications in a portable way. The
+  three main components are the Kokkos Core Programming Model, the Kokkos Kernels
+  Math Libraries and the Kokkos Profiling and Debugging Tools.
+
+  The Kokkos framework is used in several domains including climate modeling where
+  gradients are important part of the simulation process. This project aims at
+  teaching Clad to differentiate Kokkos entities in a performance portable way.
+
+  Task ideas and expected results:
+  * Implement common test cases for Kokkos in Clad
+  * Add support for Kokkos functors
+  * Add support for Kokkos lambdas
+  * Incorporate the changes from the [initial Kokkos PR](https://github.com/vgvassilev/clad/pull/783)
+  * Enhance existing benchmarks demonstrating effectiveness of Clad for Kokkos
+  * [Stretch goal] Performance benchmarks
+
+  Candidate requirements:
+    * Experience with C++, experience with ML and backpropagation
+    * Knowledge of Clang.
+contacts:
+  - name: Vaibhav Thakkar
+    email: [email protected]
+  - name: Petro Zarytskyi
+    email: [email protected]
+  - name: Vassil Vasilev
+    email: [email protected]