Exploring the Utility of Graph Methods in HPC Thermal Modeling

This work critically examines several approaches to temperature prediction for High-Performance Computing (HPC) systems, focusing on component-level and holistic models. In particular, we use publicly available data from the Tier-0 Marconi100 supercomputer and propose models ranging from a room-level Graph Neural Network (GNN) spatial model to node-level models. Our results highlight the importance of correct graph structures and suggest that while graph-based models can enhance predictions in certain scenarios, node-level models remain optimal when data is abundant. These findings contribute to understanding the effectiveness of different modeling approaches in HPC thermal prediction tasks, enabling proactive management of the modeled system.