English(EN) CHARM+: Cross-Hardware Attention with Re-Merge Multistream Mechanism

CHARM+ 机制提升大语言模型分布式注意力性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出了一种新颖的 CHARM+ 机制，旨在增强大语言模型（LLM）的分布式注意力，尤其适用于长序列。该新方法将顺序通信重组为多个级别，实现了层内并行和计算重叠。通过将跨硬件通信与层内通信解耦，CHARM+ 有效消除了带宽不平衡引起的同步开销，从而提高了 GPU 利用率和跨各种硬件架构的整体性能。评估结果表明，CHARM+ 的性能持续优于最先进的方法，在 PCIe 和 NVLink 架构上相比 Ring-Attention 实现了显著的加速。 AI

影响通过优化分布式注意力机制，提高了大语言模型的效率和可扩展性。

排序理由该条目是一篇研究论文，详细介绍了一种用于大语言模型的新技术机制。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xinhang Zhang, Boning Zhang, Chengchun Liu, Chunpu Li, Lei Sun, Weifeng Zhang, Limin Xiao · 2026-11-05 05:00

CHARM+: 跨硬件注意力与再合并多流机制

arXiv:2611.05906v1 Announce Type: new Abstract: As sequence lengths continue to grow, distributed attention based on sequence parallel (e.g., Ring-Attention) has become a mainstream solution for large language models (LLMs). Nevertheless, inherent communication dependencies limit…

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CHARM+: 跨硬件注意力与再合并多流机制

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