AI可显著提升超算系统能效，降低运行成本。
What Artificial Intelligence can do for High-Performance Computing systems?
- 用机器学习与优化算法改进超算调度与性能预测
- 基于图神经网络的异常检测准确率提升明显
- 专用语言模型在代码自动化任务上优于通用大模型
高性能计算（HPC）中心能耗巨大，带来环境与运营成本压力。本综述评估人工智能（AI），包括机器学习（ML）与优化技术，在提升实际运行中HPC系统效率方面的应用。从2019至2025年共手动筛选约1800篇文献，保留74篇‘AI for HPC’论文，并归纳为六类应用：性能估计、性能优化、调度、代理建模、故障检测与基于语言模型的自动化。调度是研究最活跃领域，涵盖基于强化学习的研究型调度器与结合机器学习与启发式规则的生产友好型混合方案。监督式性能估计是调度与优化的基础。图神经网络与时序模型通过捕捉生产环境遥测数据中的时空依赖关系，增强异常检测能力。针对HPC领域的专用语言模型在特定编码与自动化任务上表现优于通用大模型。这些成果凸显了如基于大模型的系统架构等整合机遇，并强调需推进MLOps、AI组件标准化及基准测试方法的发展。
原文摘要 · Abstract (English)
High-performance computing (HPC) centers consume substantial power, incurring environmental and operational costs. This review assesses how artificial intelligence (AI), including machine learning (ML) and optimization, improves the efficiency of operational HPC systems. Approximately 1,800 publications from 2019 to 2025 were manually screened using predefined inclusion/exclusion criteria; 74 "AI for HPC" papers were retained and grouped into six application areas: performance estimation, performance optimization, scheduling, surrogate modeling, fault detection, and language-model-based automation. Scheduling is the most active area, spanning research-oriented reinforcement-learning schedulers to production-friendly hybrids that combine ML with heuristics. Supervised performance estimation is foundational for both scheduling and optimization. Graph neural networks and time-series models strengthen anomaly detection by capturing spatio-temporal dependencies in production telemetry. Domain-specialized language models for HPC can outperform general-purpose LLMs on targeted coding and automation tasks. Together, these findings highlight integration opportunities such as LLM-based operating-system concepts and underscore the need for advances in MLOps, standardization of AI components, and benchmarking methodology.
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