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About this paper
Han Wang, Jintao Zhang, Kai Jiang, Haoxu Wang, Jianfei Chen, Jun Zhu
KernelBenchX, a new benchmark, systematically evaluates LLM-generated GPU kernels across 15 computational categories, revealing that LLMs struggle with complex numerical contracts and global semantic reasoning, and that iterative refinement primarily improves correctness rather than performance. The study found that nearly half of semantically correct kernels are slower than PyTorch baselines and exhibit poor portability across different hardware.
Key insights
- The challenges in LLM-based kernel generation are distinct, progressing from compilability and semantic correctness to hardware efficiency and performance portability, with current LLMs struggling significantly with the latter two.
- Failures often stem from LLMs' inability to reason about non-local semantic coordination and global tensor contracts, leading to errors in complex computational structures that are not merely syntactic.
- Iterative refinement strategies primarily act as repair mechanisms, improving compilability and correctness by fixing errors, but are largely ineffective at driving performance optimization.
Summary by alphaXiv.
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