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New tool Racer finds GPU kernel bugs using symbolic equivalence checking

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Racer, a new tool that uses symbolic equivalence checking to find bugs in GPU kernels. Unlike traditional differential testing, Racer analyzes kernels with symbolic inputs to generate exact algebraic expressions, distinguishing between floating-point errors and logical bugs. This method covers the entire input space and has already identified 82 real bugs in 23 libraries, including 76 previously unknown issues. AI

IMPACT This research could lead to more reliable AI services by improving the quality of underlying GPU kernel software.

RANK_REASON The cluster contains an academic paper detailing a new method and tool for finding bugs in GPU kernels. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New tool Racer finds GPU kernel bugs using symbolic equivalence checking

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The cluster contains an academic paper detailing a new method and tool for finding bugs in GPU kernels. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra
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High
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25 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tuan Truong Vu, Cheng Tan ·

    Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking

    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…

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