Implement Differentiating of the Kokkos Framework


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.

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