In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns

Harikrishna Tummalapalli; Riccardo Balin; Christine M. Simpson; Andrew Park; Aymen Alsaadi; Andrew E. Shao; Wesley Brewer; Shantenu Jha

arXiv · 2509.19150

In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns

Harikrishna Tummalapalli·Riccardo Balin·Christine M. Simpson·Andrew Park·Aymen Alsaadi·Andrew E. Shao·Wesley Brewer·Shantenu Jha

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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.

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Harikrishna Tummalapalli, Riccardo Balin, Christine M. Simpson, Andrew Park, Aymen Alsaadi, Andrew E. Shao, Wesley Brewer, Shantenu Jha. 2025-09-23. In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns. https://arxiv.org/abs/2509.19150

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