KernelBrain: Coarse-to-Fine, Budget-Aware Search for Agentic GPU Kernel Optimization

Shuai Che; Gang Peng

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arXiv · 2608.02611

KernelBrain: Coarse-to-Fine, Budget-Aware Search for Agentic GPU Kernel Optimization

Shuai Che·Gang Peng

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Abstract

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 state-of-the-art kernel agent, with up to 48% lower optimization time.

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BibTeX RIS

Shuai Che, Gang Peng. 2026-05-19. KernelBrain: Coarse-to-Fine, Budget-Aware Search for Agentic GPU Kernel Optimization. https://arxiv.org/abs/2608.02611

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