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.
Authors
- Murali Emani (ORCID: https://orcid.org/0000-0002-6279-0007)
- Ana Gainaru (ORCID: https://orcid.org/0000-0002-1375-9468)
- Shantenu Jha (ORCID: https://orcid.org/0000-0002-5040-026X)
- Frédéric Suter (ORCID: https://orcid.org/0000-0003-1902-1955)
- Wes Brewer
- Feiyi Wang (ORCID: https://orcid.org/0000-0002-0099-1559)
Institutions
- Argonne National Laboratory (US)
- Oak Ridge National Laboratory (US)
- Princeton University (US)
- Princeton Plasma Physics Laboratory (US)
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
Funders
- U.S. Department of Energy
- Battelle
- UT-Battelle
- Office of Science
- Oak Ridge National Laboratory