Minisymposium Presentation

In situ AI/ML workflows, in which ML tasks are coupled to an ongoing simulation, are an attractive new paradigm for developing robust and predictive surrogate models for accelerating time to science by steering simulation ensembles and replacing expensive computations. In the world of high performance computing (HPC), these workflows require scalable and efficient solutions to integrate the rapidly evolving ecosystem of ML frameworks with traditional simulation codes by transferring large volumes of data between the various components. To address these issues, several libraries have recently emerged from groups in industry, academia, and national labs. In this talk, we introduce SimAI-Bench – a new tool for benchmarking and comparing the performance of different coupled simulation and AI/ML workflows on current and future HPC systems. In particular, the talk will focus on workflows for in situ training of graph neural network (GNN) surrogate models from ongoing computational fluid dynamic (CFD) simulations, requiring the transfer of training data between the two components. We will discuss how different open-source libraries enable such workflows and compare their data transfer performance and scaling efficiency on the Aurora supercomputer at the Argonne Leadership Computing Facility.