Christina Koutsou

Google Summer of Code 2024 Contributor

email: christinakoutsou22[at]gmail[dot]com

Education: Integrated Master’s in Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece

Completed project: Reverse-mode automatic differentiation of GPU (CUDA) kernels using Clad
Nowadays, the rise of AI has shed light into the power of GPUs. The notion of General Purpose GPU Programming is becoming more and more popular and it seems that the scientific community is increasingly favoring it over CPU Programming. Consequently, implementation of mathematics and operations needed for such projects are getting adjusted to GPU’s architecture. Automatic differentiation is a notable concept in this context, finding applications across diverse domains from ML to Finance to Physics. 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 widen Clad’s use range and audience by enabling the reverse-mode automatic differentiation of CUDA kernels. The total goal of the project is to support the differentiation of CUDA kernels that may also include typical CUDA built-in objects (e.g. threadIdx, blockDim etc.), which are employed to prevent race conditions, using Clad. These produced kernels will compute the derivative of an argument specified by the user as the output based on an input parameter of their choosing. In addition, the user must be able to call these kernels with a custom grid configuration.

Project Proposal: URL

Mentors: Vassil Vassilev, Parth Arora, Alexander Penev

Presentations

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