HPC-AI Coupling Methodology for Scientific Applications
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Abstract (arXiv)
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.
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References · 12
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
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- 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
- 2020Directional Message Passing for Molecular Graphscited by 561
- 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 477
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- 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 313
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- 2023Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materialscited by 13
- 2023Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Datacited by 5
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