Google Summer of Code 2025 Contributor
email: aditij0205@gmail.com
Education: B.Tech in Computer Science and Engineering (AIML), Manipal Institute of Technology, Manipal, India
Ongoing project:
Implement and improve an efficient, layered tape with prefetching capabilities
Automatic Differentiation (AD) is a computational technique that enables
efficient and precise evaluation of derivatives for functions expressed in code.
Clad is a Clang-based automatic differentiation tool that transforms C++ source
code to compute derivatives efficiently. A crucial component for AD in Clad is the
tape, a stack-like data structure that stores intermediate values for reverse mode AD.
While benchmarking, it was observed that the tape operations of the current implementation
were significantly slowing down the program. This project aims to optimize and generalize
the Clad tape to improve its efficiency, introduce multilayer storage, enhance thread safety,
and enable CPU-GPU transfer.
Project Proposal: URL
Mentors: Aaron Jomy, David Lange, Vassil Vassilev
We are part of 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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