大语言模型时代的内核自动化生成探索

摘要

现代人工智能系统的性能从根本上受限于其底层内核的质量，这些内核将高级算法语义转化为底层硬件操作。实现接近最优的内核需要专家级的硬件架构和编程模型理解能力，使得内核工程成为关键但 notoriously 耗时且难以规模化的过程。基于大语言模型（LLM）的智能体技术为内核自动生成与优化开辟了新可能：LLM擅长压缩难以形式化的专家级内核知识，而智能体系统通过将内核开发转化为迭代的、反馈驱动的循环，进一步实现了可扩展的优化。该领域已取得快速进展，但目前研究仍呈碎片化状态，缺乏对LLM驱动内核生成的系统性视角。本文通过构建结构化综述填补这一空白，系统梳理了基于LLM的方法与智能体优化流程，汇编了支撑该领域学习与评估的数据集和基准测试，并进一步指出关键开放挑战与未来研究方向，旨在为新一代自动化内核优化建立全面参考框架。为追踪该领域发展，我们在GitHub上维护开源项目库：https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation。

English

The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time-consuming and non-scalable process. Recent advances in large language models (LLMs) and LLM-based agents have opened new possibilities for automating kernel generation and optimization. LLMs are well-suited to compress expert-level kernel knowledge that is difficult to formalize, while agentic systems further enable scalable optimization by casting kernel development as an iterative, feedback-driven loop. Rapid progress has been made in this area. However, the field remains fragmented, lacking a systematic perspective for LLM-driven kernel generation. This survey addresses this gap by providing a structured overview of existing approaches, spanning LLM-based approaches and agentic optimization workflows, and systematically compiling the datasets and benchmarks that underpin learning and evaluation in this domain. Moreover, key open challenges and future research directions are further outlined, aiming to establish a comprehensive reference for the next generation of automated kernel optimization. To keep track of this field, we maintain an open-source GitHub repository at https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation.

大语言模型时代的内核自动化生成探索

Towards Automated Kernel Generation in the Era of LLMs

摘要

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