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arXiv · 2602.20656

Lagom: Unleashing the Power of Communication and Computation Overlapping for Distributed LLM Training

Guanbin Xu·ZhenGuo Xu·Yuzhe Li·Youhui Bai·Ping Gong·Chaoyi Ruan·Cheng Li
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Abstract

Overlapping communication with computation is crucial for distributed large-model training, yet optimizing it - especially when computation becomes the bottleneck-remains challenging. We present Lagom, a system that co-tunes communication parameters to balance resource usage between computation and communication. By introducing a unified cost model and a priority-based search algorithm, Lagom reduces optimization complexity from exponential to linear. Evaluations on high- and low-bandwidth GPU clusters show that Lagom achieves 1.07-1.33x and 1.03-1.27x speedup over NCCL and AutoCCL across diverse models and parallelizations.

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Guanbin Xu, ZhenGuo Xu, Yuzhe Li, Youhui Bai, Ping Gong, Chaoyi Ruan, Cheng Li. 2026-02-24. Lagom: Unleashing the Power of Communication and Computation Overlapping for Distributed LLM Training. https://arxiv.org/abs/2602.20656

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