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新的Waypoint模型优化CPU上的GEMM以加速LLM推理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Waypoint，一个旨在优化CPU上通用矩阵乘法（GEMM）性能的分析模型。通过考虑内存层次结构和多线程等架构因素，Waypoint识别GEMM操作的最佳分块大小。该模型已集成到OPAM GEMM库中，该库在性能上可与现有库相媲美，并在加速CPU上大型语言模型（LLM）推理方面实现了1.36倍至1.98倍的显著提速。 AI

影响 通过优化核心计算操作来加速CPU上的LLM推理。

排序理由 该集群包含一篇研究论文，详细介绍了用于优化GEMM性能的新分析模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的Waypoint模型优化CPU上的GEMM以加速LLM推理

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该集群包含一篇研究论文，详细介绍了用于优化GEMM性能的新分析模型。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Srinidhi N, Sunanda Somwase, Hemant Kumar Chandrakant Chandavar, Eashan Dash, Varun Agrawal, Prakash Raghavendra ·

    Waypoint：CPU的GEMM分析建模

    arXiv:2610.34914v1 Announce Type: cross Abstract: High-performance General Matrix Multiplication (GEMM) implementations rely on breaking down GEMM into carefully selected tiles to achieve peak performance. We present Waypoint, a new analytical model to automatically identify the …

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