arXiv:2606.20128v1 Announce Type: cross Abstract: Benchmarks for LLM-generated GPU kernels (KernelBench, TritonBench, GEAK) score correctness through fixed-shape, small-sample allclose-style checks. The number of inputs varies between benchmarks. The shape, dtype, and tolerance are fixed for each kernel. We test that oracle empirically. We construct a controlled corpus of 24 Triton and CPU stand-in kernels (15 correct controls and 9 LLM-style buggy variants seeded with documented transcription errors) and re-evaluate it under op-schema-aware seeded fuzzing with a high-precision (fp64) CPU refe
The proliferation of LLMs in code generation for specialized hardware like GPUs makes concerns about correctness metrics immediately critical for real-world deployment.
Incorrect GPU kernel generation by LLMs poses significant risks to the reliability, safety, and performance of AI/ML systems and their underlying hardware acceleration.
Current benchmarking methodologies for LLM-generated GPU kernels are systematically flawed, requiring a fundamental re-evaluation of how such code is tested and validated.
- · Software testing and verification companies
- · GPU manufacturers focused on safety/reliability
- · Developers skilled in formal verification
- · LLM providers claiming high correctness rates without rigorous testing
- · AI/ML developers relying on unverified LLM-generated kernels
- · Benchmarks using limited 'allclose-style' checks
Demand will grow for more sophisticated and comprehensive validation tools for LLM-generated code.
AI model deployment in critical systems will face increased scrutiny regarding the correctness of low-level, generated code.
This could drive closer integration of LLMs with formal verification methods or entirely new programming paradigms emphasizing provable correctness.
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Read at arXiv cs.LG