C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption

Marcello Artioli, Andrea Borghesi, Marta Chinnici, Anna Ciampolini, Michele Colonna, Davide De Chiara and Daniela Loreti ()
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Marcello Artioli: ENEA-R.C. Bologna, 40121 Bologna, Italy
Andrea Borghesi: Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy
Marta Chinnici: ENEA-R.C. Casaccia, 00196 Rome, Italy
Anna Ciampolini: Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy
Michele Colonna: Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy
Davide De Chiara: ENEA-R.C. Portici, 80055 Portici, Italy
Daniela Loreti: Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy

Future Internet, 2025, vol. 17, issue 5, 1-18

Abstract: In recent decades, driven by global efforts towards sustainability, the priorities of HPC facilities have changed to include maximising energy efficiency besides computing performance. In this regard, a crucial open question is how to accurately predict the contribution of each parallel job to the system’s energy consumption. Accurate estimations in this sense could offer an initial insight into the overall power requirements of the system, and provide meaningful information for, e.g., power-aware scheduling, load balancing, infrastructure design, etc. While ML-based attempts employing large training datasets of past executions may suffer from the high variability of HPC workloads, a more specific knowledge of the nature of the jobs can improve prediction accuracy. In this work, we restrict our attention to the rather pervasive task of linear system resolution. We propose a methodology to build a large dataset of runs (including the measurements coming from physical sensors deployed on a large HPC cluster), and we report a statistical analysis and preliminary evaluation of the efficacy of the obtained dataset when employed to train well-established ML methods aiming to predict the energy footprint of specific software.

Keywords: high-performance computing; power consumption dataset; parallel linear solvers (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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