标题： GPU内核科学家：一种用于迭代内核优化的LLM驱动框架 显示英文标题

标题： GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization

摘要： 优化GPU内核以实现高性能是一项复杂的任务，通常需要深入的架构知识、广泛的分析和迭代实验。 当针对新型或文档不全的GPU架构时，这一挑战会进一步加剧，因为传统的开发辅助工具较为匮乏。 本文介绍了一种由大语言模型驱动的“GPU内核科学家”，这是一种用于迭代优化加速器内核的自动化方法。 我们的方法在多阶段、演进的过程中使用大语言模型：(a) 有策略地选择有前景的先前代码版本作为新迭代的基础；(b) 基于现有代码和从通用GPU文献中吸收的知识，生成优化实验的假设；(c) 通过代码修改自主实施这些实验，并将结果提交到外部评估系统，仅使用观察到的时间数据作为性能反馈。 我们详细说明了这种方法如何应对AMD MI300目标架构的挑战，并利用大语言模型弥补有限的领域特定人类专业知识。 由于在论文提交日期时，正在进行的性能竞赛的定量结果被保密，我们展示了架构设计、操作流程和定性见解，突出了大语言模型驱动的代理在民主化和加速GPU内核优化方面的潜力，尤其是在资源受限或快速发展的硬件环境中。 显示英文摘要

摘要： Optimizing GPU kernels for high performance is a complex task, often demanding deep architectural knowledge, extensive profiling, and iterative experimentation. This challenge is amplified when targeting newer or less-documented GPU architectures where traditional development aids are scarce. This paper introduces an LLM-powered "GPU Kernel Scientist," an automated methodology for iteratively refining accelerator kernels. Our methodology employs LLMs in a multi-stage, evolutionary process: (a) strategically selecting promising prior code versions as a basis for new iterations; (b) generating hypotheses for optimization experiments, based on existing code and assimilated knowledge from general GPU literature; and (c) autonomously implementing these experiments through code modification and subsequent submission to an external evaluation system, using only observed timing data as performance feedback. We detail how this approach navigates the challenges of the AMD MI300 target architecture and leverages LLMs to compensate for limited domain-specific human expertise. Since quantitative results from an ongoing performance competition were embargoed on paper submission date, we present the architectural design, operational workflow, and qualitative insights, highlighting the potential of LLM-driven agents to democratise and accelerate GPU kernel optimization, especially in resource-constrained or rapidly evolving hardware environments.