IPDPS Article

Nominated for the Best Paper Award @ IPDPS 2025, Milan, Italy.

Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstrate a streaming workflow in which simulation data is streamed directly to a machine-learning (ML) framework, circumventing the file system bottleneck. Data is transformed in transit, asynchronously to the simulation and the training of the model. With the presented workflow, data operations can be performed in common and easy-to-use programming languages, freeing the application user from adapting the application output routines. As a proof-of-concept we consider a GPU accelerated particle-in-cell (PIConGPU) simulation of the Kelvin- Helmholtz instability (KHI). We employ experience replay to avoid catastrophic forgetting in learning from this non-steady process in a continual manner. We detail challenges addressed while porting and scaling to Frontier exascale system.

Jeffrey Kelling, Vicente Bolea, Michael Bussmann, Ankush Checkervarty, Alexander Debus, Jan Ebert, Greg Eisenhauer, Vineeth Gutta, Stefan Kesselheim, Scott Klasky, Richard Pausch, Norbert Podhorszki, Franz P.schel, David Rogers, Jeyhun Rustamov, Steve Schmerler, Ulrich Schramm, Klaus Steiniger, Ren. Widera, Anna Willmann, Sunita Chandrasekaran, The Artificial Scientist: in-transit Machine Learning of Plasma Simulations, Accepted to IPDPS 2025, To presented in June 2025