Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
This work is significant for developers and researchers who need to generate correct and efficient GPU kernels, offering an incremental improvement over existing LLM-based and evolutionary methods.
This paper addresses the challenge of generating high-performance GPU kernels by proposing Kernel Foundry, an evolutionary framework. It achieves up to 100% correctness on Level 2 of KernelBench, outperforming existing methods.
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 mechanisms prevent cheating behaviors that bypass kernel-level computation. Experiments on KernelBench show that our method consistently improves both correctness and performance over strong baselines, achieving up to 100% correctness on Level~2.