The rise of ML has shed light into the power of GPUs and researchers are looking for ways to incorporate them in their projects as a lightweight parallelization method. Consequently, General Purpose GPU programming is becoming a very popular way to speed up execution time.
Clad is a clang plugin for automatic differentiation that performs source-to-source transformation and produces a function capable of computing the derivatives of a given function at compile time. This project aims to enhance Clad by adding support for Thrust, a parallel algorithms library designed for GPUs and other accelerators. By supporting Thrust, Clad will be able to differentiate algorithms that rely on Thrust’s parallel computing primitives, unlocking new possibilities for GPU-based machine learning, scientific computing, and numerical optimization.