arXiv Machine Learning By Rania Zitouni, Nadine Bousdjira, Sarah Hasnaoui, Amel Sadoun, Fatma Salhi

GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study

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arXiv:2606. 30497v1 Announce Type: cross Abstract: We present a comparative study of CUDA optimization strategies applied to forward and backward propagation in a shallow neural network.

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arXiv Machine Learning
Jun 5

CUCo: An Agentic Framework for Compute and Communication Co-design

arXiv:2603. 02376v2 Announce Type: replace-cross Abstract: Computation and communication in distributed LLM training and inference are traditionally optimized in isolation; expert-crafted systems such as DeepEP, FLUX, and TokenWeave show the potential of co-design but require deep systems expertise and hardware-specific tuning; CUCo is an agentic framework that automates compute-communication co-design of CUDA kernels by combining a structured design-space formalization with a correctness-first fast-path agent for reliable baselines and an evolution-driven slow-path agent for high-performance strategies, achieving up to 1.

By Yoga Sri Varshan Varadharajan, Bodun Hu, Saurabh Agarwal, Aditya Akella