提出三种融合HPC与AI的新方法，提升科学计算效率。
HPC-AI Coupling Methodology for Scientific Applications
- 构建代理、指导、协同三类耦合模式，实现AI与高性能计算协同。
- 在材料科学案例中验证，显著降低计算成本并提升精度。
- 适用于多领域科学发现，为未来科研提供可复用框架。
人工智能技术正通过数据驱动方法深刻改变基于数值的高性能计算（HPC）应用，致力于解决高计算强度等挑战。本研究探索新兴科学应用中HPC与AI耦合（HPC-AI）的场景，提出一种新方法论，包含三种耦合模式：代理型、指导型和协同型。每种模式对应不同的耦合策略、AI驱动前提及典型HPC-AI集成方式。通过材料科学领域的案例研究，验证了这些模式的应用效果。研究揭示了关键技术挑战、性能提升情况及实现细节，为HPC-AI耦合提供了前景洞察。所提耦合模式不仅适用于材料科学，还可推广至其他科学领域，为未来科学发现中的HPC-AI集成提供重要指导。
原文摘要 · Abstract (English)
Artificial intelligence (AI) technologies have fundamentally transformed numerical-based high-performance computing (HPC) applications with data-driven approaches and endeavored to address existing challenges, e.g. high computational intensity, in various scientific domains. In this study, we explore the scenarios of coupling HPC and AI (HPC-AI) in the context of emerging scientific applications, presenting a novel methodology that incorporates three patterns of coupling: surrogate, directive, and coordinate. Each pattern exemplifies a distinct coupling strategy, AI-driven prerequisite, and typical HPC-AI ensembles. Through case studies in materials science, we demonstrate the application and effectiveness of these patterns. The study highlights technical challenges, performance improvements, and implementation details, providing insight into promising perspectives of HPC-AI coupling. The proposed coupling patterns are applicable not only to materials science but also to other scientific domains, offering valuable guidance for future HPC-AI ensembles in scientific discovery.
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。