{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:45:38Z","timestamp":1783075538628,"version":"3.54.6"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031695766","type":"print"},{"value":"9783031695773","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-69577-3_11","type":"book-chapter","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:02:05Z","timestamp":1724612525000},"page":"152-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Light-Weight Prediction for\u00a0Improving Energy Consumption in\u00a0HPC Platforms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-8137","authenticated-orcid":false,"given":"Danilo","family":"Carastan-Santos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3365-7709","authenticated-orcid":false,"given":"Georges","family":"Da Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1368-5016","authenticated-orcid":false,"given":"Millian","family":"Poquet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5169-1831","authenticated-orcid":false,"given":"Patricia","family":"Stolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2623-6922","authenticated-orcid":false,"given":"Denis","family":"Trystram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Antici, F., Yamamoto, K., Domke, J., Kiziltan, Z.: Augmenting ml-based predictive modelling with NLP to forecast a job\u2019s power consumption. In: Proceedings of the SC\u201923 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, pp. 1820\u20131830 (2023)","DOI":"10.1145\/3624062.3624264"},{"issue":"2","key":"11_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s00287-014-0850-0","volume":"38","author":"N Bates","year":"2015","unstructured":"Bates, N., et al.: Electrical grid and supercomputing centers: an investigative analysis of emerging opportunities and challenges. Informatik-Spektrum 38(2), 111\u2013127 (2015)","journal-title":"Informatik-Spektrum"},{"key":"11_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/978-3-319-41321-1_10","volume-title":"High Performance Computing","author":"A Borghesi","year":"2016","unstructured":"Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., Benini, L.: Predictive modeling for job power consumption in HPC systems. In: Kunkel, J.M., Balaji, P., Dongarra, J. (eds.) ISC High Performance 2016. LNCS, vol. 9697, pp. 181\u2013199. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41321-1_10"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s41597-023-02174-3","volume":"10","author":"A Borghesi","year":"2023","unstructured":"Borghesi, A., et al.: M100 ExaData: a data collection campaign on the CINECA\u2019s marconi100 tier-0 supercomputer. Sci. Data 10(1), 288 (2023)","journal-title":"Sci. Data"},{"issue":"3","key":"11_CR5","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/sam.11339","volume":"10","author":"B Bugbee","year":"2017","unstructured":"Bugbee, B., Phillips, C., Egan, H., Elmore, R., Gruchalla, K., Purkayastha, A.: Prediction and characterization of application power use in a high-performance computing environment. Stat. Anal. Data Mining ASA Data Sci. J. 10(3), 155\u2013165 (2017)","journal-title":"Stat. Anal. Data Mining ASA Data Sci. J."},{"issue":"10","key":"11_CR6","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.1016\/j.jpdc.2014.06.008","volume":"74","author":"H Casanova","year":"2014","unstructured":"Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899\u20132917 (2014)","journal-title":"J. Parallel Distrib. Comput."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Chasapis, D., Moret\u00f3, M., Schulz, M., Rountree, B., Valero, M., Casas, M.: Power efficient job scheduling by predicting the impact of processor manufacturing variability. In: Proceedings of the ACM International Conference on Supercomputing, pp. 296\u2013307 (2019)","DOI":"10.1145\/3330345.3330372"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.suscom.2017.05.003","volume":"15","author":"G Da Costa","year":"2017","unstructured":"Da Costa, G., Pierson, J.M., Fontoura-Cupertino, L.: Mastering system and power measures for servers in datacenter. Sustain. Comput. Inform. Syst. 15, 28\u201338 (2017). https:\/\/doi.org\/10.1016\/j.suscom.2017.05.003","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Dutot, P.F., Mercier, M., Poquet, M., Richard, O.: Batsim: a realistic language-independent resources and jobs management systems simulator. In: 20th Workshop on Job Scheduling Strategies for Parallel Processing, Chicago, United States (2016). https:\/\/hal.science\/hal-01333471","DOI":"10.1007\/978-3-319-61756-5_10"},{"key":"11_CR10","unstructured":"Emeras, J.: Workload Traces Analysis and Replay in Large Scale Distributed Systems. Theses, Universit\u00e9 de Grenoble (2013)"},{"key":"11_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-63574-2_14","volume-title":"Job Scheduling Strategies for Parallel Processing","author":"DG Feitelson","year":"1997","unstructured":"Feitelson, D.G., Rudolph, L., Schwiegelshohn, U., Sevcik, K.C., Wong, P.: Theory and practice in parallel job scheduling. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1997. LNCS, vol. 1291, pp. 1\u201334. Springer, Heidelberg (1997). https:\/\/doi.org\/10.1007\/3-540-63574-2_14"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Feitelson, D.G., Weil, A.M.: Utilization and predictability in scheduling the IBM SP2 with backfilling. In: Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing, pp. 542\u2013546. IEEE (1998)","DOI":"10.1109\/IPPS.1998.669970"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Gaussier, E., Glesser, D., Reis, V., Trystram, D.: Improving backfilling by using machine learning to predict running times. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. SC 2015. Association for Computing Machinery, New York (2015)","DOI":"10.1145\/2807591.2807646"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Khan, K.N., Hirki, M., Niemi, T., Nurminen, J.K., Ou, Z.: RAPL in action: experiences in using RAPL for power measurements. ACM Trans. Model. Perform. Eval. Comput. Syst. 3(2) (2018). https:\/\/doi.org\/10.1145\/3177754","DOI":"10.1145\/3177754"},{"issue":"2","key":"11_CR15","doi-asserted-by":"publisher","first-page":"890","DOI":"10.3390\/en16020890","volume":"16","author":"B Kocot","year":"2023","unstructured":"Kocot, B., Czarnul, P., Proficz, J.: Energy-aware scheduling for high-performance computing systems: a survey. Energies 16(2), 890 (2023)","journal-title":"Energies"},{"key":"11_CR16","unstructured":"Oak Ridge National Laboratory: Frontier\u2019s architecture (2023). https:\/\/olcf.ornl.gov\/wp-content\/uploads\/Frontiers-Architecture-Frontier-Training-Series-final.pdf. Accessed 29 Nov 2023"},{"key":"11_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR18","doi-asserted-by":"publisher","unstructured":"Poquet, M., Carastan-Santos, D., Da\u00a0Costa, G., Stolf, P., Trystram, D.: Artifact data of article \u201clight-weight prediction for improving energy consumption in HPC platforms. Euro-Par 2024 (2024). https:\/\/doi.org\/10.5281\/zenodo.11173631","DOI":"10.5281\/zenodo.11173631"},{"key":"11_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-50743-5_4","volume-title":"High Performance Computing","author":"T Saillant","year":"2020","unstructured":"Saillant, T., Weill, J.-C., Mougeot, M.: Predicting job power consumption based on RJMS submission data in HPC systems. In: Sadayappan, P., Chamberlain, B.L., Juckeland, G., Ltaief, H. (eds.) ISC High Performance 2020. LNCS, vol. 12151, pp. 63\u201382. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50743-5_4"},{"issue":"2","key":"11_CR20","first-page":"20","volume":"1","author":"H Shoukourian","year":"2014","unstructured":"Shoukourian, H., Wilde, T., Auweter, A., Bode, A.: Predicting the energy and power consumption of strong and weak scaling HPC applications. Supercomput. Front. Innovations 1(2), 20\u201341 (2014)","journal-title":"Supercomput. Front. Innovations"},{"key":"11_CR21","unstructured":"Storlie, C., Sexton, J., Pakin, S., Lang, M., Reich, B., Rust, W.: Modeling and predicting power consumption of high performance computing jobs (2015)"},{"key":"11_CR22","unstructured":"Wikipedia: 2021 Texas power crisis (2023). https:\/\/en.wikipedia.org\/wiki\/2021_Texas_power_crisis. Accessed 29 Nov 2023"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jpdc.2022.01.003","volume":"164","author":"S Zrigui","year":"2022","unstructured":"Zrigui, S., de Camargo, R.Y., Legrand, A., Trystram, D.: Improving the performance of batch schedulers using online job runtime classification. J. Parallel Distrib. Comput. 164, 83\u201395 (2022)","journal-title":"J. Parallel Distrib. Comput."}],"container-title":["Lecture Notes in Computer Science","Euro-Par 2024: Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-69577-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T06:05:45Z","timestamp":1732687545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-69577-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031695766","9783031695773"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-69577-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Euro-Par","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"europar2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.euro-par.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}