The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: The 1st International Workshop on the Environmental Sustainability of High-Performance Software
Authors: Francesco Antici (University of Bologna), Keiji Yamamoto and Jens Domke (RIKEN Center for Computational Science (R-CCS)), and Zeynep Kiziltan (University of Bologna)
Abstract: As modern High-Performance Computing (HPC) reach exascale performance, their power consumption becomes a serious threat to environmental and energy sustainability. Efficient power management in HPC systems is crucial for optimizing workload management, reducing operational costs, and promoting environmental sustainability. Accurate prediction of job power consumption plays an important role in achieving such goals. We apply a technique combining Machine Learning (ML) algorithms with Natural Language Processing (NLP) tools to predict job power consumption. The solution is able to predict job maximum and average power consumption per node, leveraging only information which is available at the time of job submission. The prediction is performed in an online fashion, and we validate the approach using batch system logs extracted from Supercomputer Fugaku. The experimental evaluation shows promising results of outperforming classical technique while obtaining an R2 score of more than 0.53 for our two prediction tasks.