arXiv AI
Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking
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Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles
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.
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The Correctness Illusion in LLM-Generated GPU Kernels
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AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification
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arXiv AI
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M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification
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The Output-Space Hypothesis: Enumerative Equivalence Checking for Tensor Programs
arXiv:2609. 19611v1 Announce Type: cross Abstract: Tensor programs, as used in deep learning models, are a prime target for optimization, as small performance improvements can have a large impact across training or inference workloads.
arXiv AI
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