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English(EN) Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking

新工具 Racer 通过符号等价性检查查找 GPU 内核错误

研究人员开发了 Racer,一个使用符号等价性检查来查找 GPU 内核错误的新工具。与传统的差分测试不同,Racer 使用符号输入分析内核,生成精确的代数表达式,区分浮点错误和逻辑错误。该方法覆盖了整个输入空间,并已在 23 个库中识别出 82 个真实错误,包括 76 个先前未知的错误。 AI

影响 这项研究通过提高底层 GPU 内核软件的质量,可能带来更可靠的 AI 服务。

排序理由 该集群包含一篇学术论文,详细介绍了一种发现 GPU 内核错误的新方法和工具。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新工具 Racer 通过符号等价性检查查找 GPU 内核错误

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该集群包含一篇学术论文,详细介绍了一种发现 GPU 内核错误的新方法和工具。[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) · Tuan Truong Vu, Cheng Tan ·

    Racer:使用符号等价性检查来查找 GPU 内核错误

    arXiv:2610.28429v1 Announce Type: cross Abstract: GPU kernels are critical to the performance of today's AI services, yet the same operators are rewritten repeatedly: for each GPU generation, numeric format, serving regime, and kernel language. Every rewrite must preserve the fun…