English(EN) CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems

新的CTA-pipelining方法大幅降低LLM的多GPU延迟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-10 04:00

研究人员推出了一种新颖的多GPU系统执行范式CTA-pipelining，该范式针对服务大型语言模型的延迟进行了优化。该方法利用了协同线程阵列（Cooperative Thread Array）层面的依赖关系，从而实现跨GPU的并发内核执行。在H200和B200系统上的实验表明，CTA-pipelining可以将特定操作的延迟降低高达31.8%，并且可以与张量并行（Tensor Parallelism）结合以获得进一步的性能提升。 AI

影响可能显著降低大型语言模型的推理延迟，从而实现更快、响应更灵敏的AI应用。

排序理由详细介绍一种优化多GPU系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tingkai Liu, Muralidhar Andoorveedu, Sanjoy Das, Sanjay Patel, Volodymyr Kindratenko · 2026-07-10 04:00

CTA-Pipelining：面向延迟的多GPU系统空间扩展方法

arXiv:2607.07862v1 Announce Type: cross Abstract: The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures. However, current software still mostly treats these coherent interconnects simply as high-speed networks. …

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报道来源 [1]

CTA-Pipelining：面向延迟的多GPU系统空间扩展方法

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