AI-coupled HPC Workflow Applications, Middleware and Performance

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Authors: Wes Brewer, Ana Gainaru, Frédéric Suter, Feiyi Wang, Murali Emani, Shantenu Jha
Cited by: 11
References: 184

Abstract

AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-coupled HPC workflows. This paper surveys the diverse and rapidly evolving field of AI-driven HPC and provides a common conceptual basis for understanding such workflows. Specifically, we use insights from different modes of coupling AI into HPC workflows to propose six execution motifs most commonly found in scientific applications. By definition, the proposed set of execution motifs is incomplete and evolving. However, they allow us to analyze the primary performance challenges across AI-integrated simulation environments. We close with a listing of open challenges, research issues, and suggested areas of investigation, including the the need for specific benchmarks that will help evaluate and improve the execution of AI-coupled HPC workflows.

Fields of Study & Subjects

Distributed and Parallel Computing Systems Scientific Computing and Data Management Advanced Data Storage Technologies

Publication details

Journal / source
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Publication date
9 September 2026
Language
EN