Towards Automated Kernel Generation in the Era of LLMs

TL;DR

This paper surveys recent advances in automating kernel generation using large language models and agentic systems, highlighting current approaches, datasets, benchmarks, challenges, and future directions.

Contribution

It provides a systematic overview of LLM-driven kernel generation methods, compiling key datasets and benchmarks, and outlines open challenges for future research.

Findings

LLMs can effectively encode expert kernel knowledge

Agentic systems enable iterative, feedback-driven kernel optimization

The field is rapidly evolving but lacks a unified framework

Abstract

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.