arXiv Machine Learning June 5, 2026 By Yoga Sri Varshan Varadharajan, Bodun Hu, Saurabh Agarwal, Aditya Akella

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

Read the original on arXiv Machine Learning →

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.

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