- By:
- Tummalapalli, Harikrishna; Balin, Riccardo; Simpson, Christine; Park, Andrew; Alsaadi, Aymen; Shao, Andrew; Brewer, Wesley H; Jha, Shantenu
- Page Number:
- 985-996
- Book Title:
- Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publication Date:
- March 12, 2026
- Publisher Location:
- Association for Computing Machinery, New York, New York, United States of America
- Conference Name:
- International Conference for High Performance Computing, Networking, Storage and Analysis (SC '25) Workshop: In Situ AI, Analysis and Visualization ( ISAV 2025)
- Conference Location:
- St. Louis, Missouri, United States of America
- Conference Sponsor:
- ACM
- View DOI Listing:
- https://doi.org/10.1145/3731599.3767464
Abstract
Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool designed to both prototype and evaluate these coupled workflows. In this paper, we use SimAI-Bench to benchmark the data transport performance of two common patterns on the Aurora supercomputer: a one-to-one workflow with co-located simulation and AI training instances, and a many-to-one workflow where a single AI model is trained from an ensemble of simulations. For the one-to-one pattern, our analysis shows that node-local and DragonHPC data staging strategies provide excellent performance compared Redis and Lustre file system. For the many-to-one pattern, we find that data transport becomes a dominant bottleneck as the ensemble size grows. Our evaluation reveals that file system is the optimal solution among the tested strategies for the many-to-one pattern.
