KernelBrain: Coarse-to-Fine, Budget-Aware Search for Agentic GPU Kernel Optimization
Automating GPU kernel optimization remains difficult in practice: generated variants can violate correctness constraints, runtime measurements are noisy, and search often stalls early. We present a practical optimization agent that combines LLM-guided mutation, adaptive resource allocation, policy-gated evaluation, and profiler-informed diagnosis. The system screens many candidates with low-cost evaluation and allocates higher-fidelity budget only to promising survivors to optimize and evolve GPU kernels. On important Triton kernel generation tasks, this design improves both kernel quality and search efficiency, reaching 0.88x-6.72x speedup over PyTorch and up to 1.4x speedup over the sta...
| Authors | Shuai Che, Gang Peng |
|---|---|
| Primary category | cs.DC |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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