Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language models (LLMs) show promise in code generation, they often fail to produce kernels that are both correct and efficient. We propose Kernel Foundry, a diagnosis-driven evolutionary framework for automatic GPU kernel optimization. Our method combines expert-guided, retrieval-augmented initialization with a multi-island evolutionary search, where candidate kernels are iteratively refined using structured diagnostic feedback. A centralized experience library accumulates reusable optimization knowledge to guide subsequent evolution, while explicit...
| Authors | Zixuan Huang, Da Chen, Kecheng Huang et al. |
|---|---|
| Primary category | cs.NE |
| Published | 8 May 2026 |
| Last updated at source | 3 August 2026 |
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