ACM Computing Surveys· 2026Q1
AI-coupled HPC Workflow Applications, Middleware and Performance
- 11citations
- Q1SCImago
- 2026year
Short summary
This survey identifies six common execution motifs for AI-coupled High-Performance Computing (HPC) workflows, enabling analysis of performance challenges in AI-integrated simulations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Identifies six common execution motifs for AI-coupled HPC workflows.
- Provides a conceptual basis for understanding AI-driven HPC.
- Enables analysis of performance challenges in AI-integrated simulations.
- Highlights the need for specific benchmarks for AI-coupled HPC.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. ACM Computing Surveys, 2026 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science