Towards Automated Kernel Generation in the Era of LLMs
🤖 AI Summary
This work addresses the critical bottleneck in modern AI systems—operator performance—whose optimization traditionally relies on expert-crafted implementations that are costly and difficult to scale. To advance this field, the paper presents the first systematic survey of large language model (LLM) and agent-driven approaches for automated operator generation. It introduces a unified framework that integrates existing methodologies, datasets, and evaluation benchmarks, complemented by a structured taxonomy to categorize current techniques. Furthermore, the authors maintain an open-source repository of curated resources, offering a comprehensive reference for researchers. This effort aims to foster standardization, reproducibility, and future innovation in the emerging domain of automated high-performance operator synthesis.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
kernel generation
LLM-driven automation
hardware optimization
expert knowledge compression
scalable kernel engineering
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-driven kernel generation
agentic optimization
automated code generation
kernel optimization
AI for systems
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