KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
GPU kernel optimization challenges LLMs beyond standard coding tasks, as it requires an understanding of hardware architecture, parallel computing optimization strategies, and profiling outputs. However, most existing approaches leveraging LLMs for kernel generation apply standard prompting and feedback loops, considering hardware only through profiling feedback. We introduce KernelFoundry, an evolutionary framework that efficiently explores the space of GPU kernels through (1) MAP-Elites quality diversity search with kernel-specific behavioral dimensions to sustain exploration; (2) meta-prompt evolution that co-evolves prompts with kernels to uncover task-specific optimization strategies...
| Authors | Nina Wiedemann, Quentin Leboutet, Michael Paulitsch et al. |
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| Primary category | cs.DC |
| Published | 12 March 2026 |
| Last updated at source | 3 September 2026 |
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