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arXiv Machine Learning By Mingzhe Du, Anh Tuan Luu, Dong Huang, See-Kiong Ng

Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles

Read the original on arXiv Machine Learning →

The paper introduces mutation analysis as a metric for evaluating GPU‑kernel benchmark oracles, injecting over ten thousand faults into verified CUDA implementations of 188 KernelBench problems. It shows that the current official checkers miss 16.9% of faults, with precision faults being especially problematic, and demonstrates that optimized test suites can achieve 98% detection with only two inputs per problem. The study also reveals flaws in existing patches and a fuzzing recipe that incorrectly rejects correct kernels 107 times.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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