用多智能体自动优化超算代码，无需人工干预即可提升性能。
VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs
- 多智能体协作：项目经理、系统工程师等分工配合，实现自主编程。
- 自动优化成功将CPU代码在GPU上提速，矩阵乘法与泊松求解器性能显著提升。
- 适合超算开发者快速生成高性能代码，降低对专业人才依赖。
本文提出VibeCodeHPC，一个基于大语言模型（LLMs）的多智能体系统，用于在超级计算机上自动调优高性能计算（HPC）程序。该系统以Claude Code为后端，提供适配超算环境的集成开发环境。VibeCodeHPC引入‘Vibe编码’范式，通过自然语言交互实现编程，并借助复杂的多智能体设计，实现最小用户干预下的自主性能优化。其核心功能包括：（1）针对超算独特开发环境的配置能力；（2）由项目管理、系统工程、程序员和持续交付四类角色构成的多智能体协同机制；（3）通过活动监控与动态部署实现长期自主运行。论文重点展示了系统在无用户干预下全自动优化代码的能力：成功将基于CPU的矩阵乘法和雅可比迭代法求解泊松方程的代码，在配备GPU的系统上完成性能优化。结果表明，相比单智能体设置，多智能体配置能更快速、更可靠地生成高性能代码。
原文摘要 · Abstract (English)
In this study, we propose VibeCodeHPC, a multi-agent system based on large language models (LLMs) for the automatic tuning of high-performance computing (HPC) programs on supercomputers. VibeCodeHPC adopts Claude Code as its backend and provides an integrated environment that facilitates program development in supercomputer settings. The system not only brings the Vibe Coding paradigm -- program development through natural language interaction with users -- to HPC programming, but also enables autonomous performance optimization with minimal user intervention through a sophisticated multi-agent design. To achieve these objectives, VibeCodeHPC implements three core functionalities: (1) configuration capabilities tailored to the unique development environments of supercomputers, (2) collaborative operation among multiple LLM agents with distinct roles -- Project Manager (PM), System Engineer (SE), Programmer (PG), and Continuous Deliverer (CD), and (3) long-term autonomous operation through agent activity monitoring and dynamic deployment mechanisms. This paper highlights one of the most powerful features of VibeCodeHPC: fully automated code optimization through autonomous operation without user intervention. Specifically, it demonstrates the performance optimization of CPU-based codes on GPU-equipped systems for matrix multiplication and a Poisson equation solver using Jacobi's iterative method. The results show that the multi-agent configuration employed in VibeCodeHPC enables faster and more reliable development of higher-performance code compared to a single-agent setup.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。