Intel XPU Kernel Skill: LLM-driven Triton kernel optimization for the Hugging Face Kernel Hub

Xe-Forge is an Intel project designed to optimize Triton kernels for Intel Arc Pro GPUs using a sequence of optimization stages driven by a large language model (LLM). This process, called CoVeR (Chain-of-Verification-and-Refinement), involves a loop that tests and iterates kernel candidates on the GPU to enhance performance. The Xe-Forge framework leverages a knowledge base of Intel XPU-specific patterns to guide optimization, which is often underrepresented in LLM training data. On the Intel Arc Pro B70, Xe-Forge achieves significant speedups over existing PyTorch and Triton kernels, demonstrating its ability to enhance even hand-tuned kernels. The xpu-kernels skill packages this optimization process into an Agent Skill, allowing a coding agent to perform the optimization loop without requiring the entire project. Xe-Forge's effectiveness has been proven across various kernel configurations, particularly in memory and compute-bound scenarios, and it emphasizes the importance of knowledge access in optimizing kernels for less-represented architectures like Intel's XPU.