{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:00:41Z","timestamp":1775671241968,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/fi17050203","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T06:49:02Z","timestamp":1746082142000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers\u2019 Power Consumption"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8398-8808","authenticated-orcid":false,"given":"Marcello","family":"Artioli","sequence":"first","affiliation":[{"name":"ENEA-R.C. Bologna, 40121 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2298-2944","authenticated-orcid":false,"given":"Andrea","family":"Borghesi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8123-2791","authenticated-orcid":false,"given":"Marta","family":"Chinnici","sequence":"additional","affiliation":[{"name":"ENEA-R.C. Casaccia, 00196 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9314-1958","authenticated-orcid":false,"given":"Anna","family":"Ciampolini","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4983-1107","authenticated-orcid":false,"given":"Michele","family":"Colonna","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4882-2401","authenticated-orcid":false,"given":"Davide","family":"De Chiara","sequence":"additional","affiliation":[{"name":"ENEA-R.C. Portici, 80055 Portici, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6507-7565","authenticated-orcid":false,"given":"Daniela","family":"Loreti","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"ref_1","unstructured":"Malms, M., Cargemel, L., Suarez, E., Mittenzwey, N., Duranton, M., Sezer, S., Prunty, C., Ross\u00e9-Laurent, P., P\u00e9rez-Harnandez, M., and Marazakis, M. (2022). ETP4HPC\u2019s SRA 5\u2014Strategic Research Agenda for High-Performance Computing in Europe\u20142022. Zenodo."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gupta, U., Kim, Y.G., Lee, S., Tse, J., Lee, H.H.S., Wei, G.Y., Brooks, D., and Wu, C.J. (March, January 27). Chasing carbon: The elusive environmental footprint of computing. 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