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arXiv · 2603.02376

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

Yoga Sri Varshan Varadharajan·Bodun Hu·Saurabh Agarwal·Aditya Akella
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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 1.57x speedup across four multi-GPU workloads and discovering a two-stream overlap strategy on a DeepSeek-V3 MoE layer that hides dispatch behind local compute at an LLM inference cost under $10 per workload.

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Yoga Sri Varshan Varadharajan, Bodun Hu, Saurabh Agarwal, Aditya Akella. 2026-03-02. CUCo: An Agentic Framework for Compute and Communication Co-design. https://arxiv.org/abs/2603.02376

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