Abdelrhman Elrawy

Google Summer of Code 2026 Contributor

email: abdelrhman[dot]elrawy1[at]gmail[dot]com

Education: Master of Applied Computing, Wilfrid Laurier University, Canada

Ongoing project: Enabling Differentiable Rendering via AD of Parallel C++ STL Primitives in Clad
This project proposes to enhance Clad, a Clang-based automatic differentiation (AD) tool, by leveraging its liveness analysis to automatically generate lock-free backward passes for highly parallel differentiable rendering pipelines. Specifically, the project targets the atomic bottleneck inherent in 3D Gaussian Splatting (3DGS) rasterization.

Project Proposal: URL

Mentors: Vassil Vassilev, Alexander Penev

Completed project: Support usage of Thrust API in Clad
This project enhances Clad, a Clang-based automatic differentiation tool, by enabling it to support NVIDIA’s Thrust library for GPU-parallel programming. The goal is to implement custom derivative rules for Thrust primitives like thrust::transform and thrust::reduce, making it possible to differentiate high-performance CUDA code automatically. This work bridges the gap between automatic differentiation and GPU acceleration, enabling efficient gradient computations in scientific computing and machine learning workloads.

Project Proposal: URL

Mentors: Vassil Vassilev, Alexander Penev

Presentations

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