HPC-AI Coupling Methodology for Scientific Applications

TL;DR

This paper introduces a novel HPC-AI coupling methodology with three distinct patterns, demonstrated through materials science case studies, addressing technical challenges and enhancing performance in scientific applications.

Contribution

The paper presents a new HPC-AI coupling framework with three patterns—surrogate, directive, and coordinate—that improve scientific computing efficiency.

Findings

Effective application of coupling patterns in materials science

Performance improvements in HPC-AI applications

Guidance for future HPC-AI ensemble development

Abstract

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.