AI-coupled HPC Workflow Applications, Middleware and Performance

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.

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Publication Details

Journal
ACM Computing Surveys
Published
2026-09-09
DOI
https://doi.org/10.1145/3844499
Citations
11
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
12.16

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article

AI-coupled HPC Workflow Applications, Middleware and Performance

Murali Emani, Ana Gainaru, Shantenu Jha, Frédéric Suter et al.
11 citations
ACM Computing Surveys
Distributed and Parallel Computing Systems
12.16
article

AI-coupled HPC Workflow Applications, Middleware and Performance

Murali Emani, Ana Gainaru, Shantenu Jha, Frédéric Suter, Wes Brewer, Feiyi Wang
article en
11 citations

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.

ACM Computing Surveys
Argonne National Laboratory (US), Oak Ridge National Laboratory (US), Princeton University (US), Princeton Plasma Physics Laboratory (US)
U.S. Department of Energy, Battelle, UT-Battelle, Office of Science, Oak Ridge National Laboratory
Openalex Percentile: Top 3%
Distributed and Parallel Computing Systems
12.16
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