AI RESEARCH
Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts
arXiv CS.LG
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ArXi:2603.07169v1 Announce Type: new Optimizing GPU kernels manually is a challenging and time-consuming task. With the rapid development of LLMs, automated GPU kernel optimization is gradually becoming a tangible reality. However, current LLM-driven automated optimization methods narrowly focus on machine learning applications, such as PyTorch operator optimization, while overlooking broader domains like sparse matrix operations in scientific computing. Extending to these broader applications brings new challenges for the benchmark and algorithm.