The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: ISAV25: In Situ AI, Analysis, and Visualization
Authors: Colleen Heinemann (University of Illinois at Urbana-Champaign); Jens Henrik Göbbert and Mathis Bode (Forschungzentrum Juelich, Jülich Supercomputing Centre (JSC)); Mengjiao Han, Joseph A. Insley, Silvio Rizzi, and Janet Knowles (Argonne National Laboratory (ANL)); Michael E. Papka (Argonne National Laboratory (ANL), University of Illinois Chicago); and Victor A. Mateevitsi (Argonne National Laboratory (ANL))
Abstract: High Performance Computing (HPC) simulations often run on GPUs while in situ rendering tasks are offloaded to CPUs. Deciding how frequently to perform these in situ renderings is a challenge: render too frequently, and the simulation, or producer, oversaturates the rendering pipeline, or consumer; render too sparsely, and the CPU resources remain idle. This project seeks to develop machine learning (ML) models that can predict rendering times based on available system resources as well as simulation parameters. By leveraging ML-driven insight, the goal of this project is to analyze the tradeoffs and determine an optimal rendering interval for simulations such as nekRS instrumented with Ascent, in this instance. and ensure balanced workloads between the simulation and rendering tasks, ultimately improving overall computational efficiency.
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