Google Summer of Code 2026 Contributor
email: goyalvedant2005[at]gmail[dot]com
Education: B.E. in Electrical and Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, India
Ongoing project:
Consolidate and advance the GPU infrastructure in Clad
Automatic Differentiation(AD) is a set of techniques to evaluate the derivative
of functions specified by the computer programs precisely and efficiently. Clad
is a Clang based AD tool that transforms C++ code to compute derivatives. Clad
supports multiple differentiation modes like reverse mode, forward mode and hessian
mode making it suitable for wide range of applications. Clad have also demonstrated
promising support for GPU based differentiation however the work remains fragmented.
This project aims to consolidate the fragmented work and advance Clad’s GPU infrastructure
into a robust and consistent system.
Project Proposal: URL
Mentors: Aaron Jomy, David Lange, Vassil Vassilev
We are partially funded by the
Princeton Department of Physics
and
Princeton Institute for Computational Science and Engineering
(PICSciE).
Contact: David Lange; Vassil Vassilev
This project is supported by the National Science Foundation under Grant OAC-1931408 (2020-2023) and OAC-2311471(2023-2026). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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