CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training
The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, TPUs, and NPUs. However, these parallelization strategies incur substantial data communication overhead significantly hindering computational efficiency. While communication-computation overlap presents a promising direction, existing data slicing based solutions suffer from tail latency. To overcome this limitation, this research introduces a novel communication-computation overlap technique to eliminate this tail latency in state of the art overlap methods for distributed LLM training. The aim of this technique is to effectively mitigate co...
| Authors | Rezaul Karim, Austin Wen, Wang Zongzuo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 29 April 2026 |
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