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English(EN) KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

KernelFoundry框架为LLM优化GPU核

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了KernelFoundry，一个旨在优化大型语言模型GPU核的演化式框架。该系统利用MAP-Elites进行质量多样性搜索，通过元提示演化发现任务特定的优化策略，并进行基于模板的参数调优以适应硬件和输入。KernelFoundry在SYCL核的KernelBench基准测试中持续优于基线方法，平均提速2.3倍，并能生成CUDA核。 AI

影响 这项研究可能带来在专用硬件上更高效的AI模型执行。

排序理由 该集群包含一篇学术论文，详细介绍了一种新的GPU核优化方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

KernelFoundry框架为LLM优化GPU核

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该集群包含一篇学术论文，详细介绍了一种新的GPU核优化方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nina Wiedemann, Quentin Leboutet, Michael Paulitsch, Diana Wofk, Benjamin Ummenhofer ·

    KernelFoundry：硬件感知式进化 GPU 核函数优化

    arXiv:2603.12440v2 Announce Type: replace-cross Abstract: GPU kernel optimization challenges LLMs beyond standard coding tasks, as it requires an understanding of hardware architecture, parallel computing optimization strategies, and profiling outputs. However, most existing appr…

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