IDLEWiSE. A Project Concept for AI-Assisted Energy Efficiency in HPC Clusters

dc.contributor.authorBassini, Chiara Fusar
dc.contributor.authorHackel, Leonard
dc.contributor.authorKirschbaum, Thorren
dc.date.accessioned2025-03-10T10:10:54Z
dc.date.available2025-03-10T10:10:54Z
dc.date.issued2025-03-10
dc.description.abstractThe growing energy demand for high-performance computing (HPC) systems raises severe concerns about their environmental impact. Novel system paradigms and computational schemes are needed to limit energy consumption while ensuring the efficiency and availability of computing resources. In this contribution, we introduce a concept for an Intelligent Decision Tool for Lowering Energy Waste in System Efficiency (IDLEWiSE), which aims to decrease the energy consumption of HPC clusters operating below total capacity by selectively shutting down idle computational units. This paper outlines an optimization tool using efficient machine-learning algorithms like decision trees to learn optimal shutdown policies online. We further locate our approach in the context of existing energy-economizing instruments and perform a strategic analysis and stepwise validation of the proposed concept. The study also includes qualitative anonymized findings from a survey of German scientific HPC cluster administrators, corroborating the urgent need for energy-efficient tools and practices for practitioners.
dc.description.sponsorshipThe Weizenbaum Institute is funded by the German Federal Ministry of Education and Research (BMBF)
dc.identifier.citationFusar Bassini, C., Hackel, L., & Kirschbaum, T. (2025). IDLEWiSE: A Project Concept for AI-Assisted Energy Efficiency in HPC Clusters. Weizenbaum Journal of the Digital Society, 5(1). https://doi.org/10.34669/wi.wjds/5.1.5
dc.identifier.doi10.34669/wi.wjds/5.1.5
dc.identifier.issn2748-5625
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/851
dc.identifier.zdb3064083-0
dc.language.isoeng
dc.publisherWeizenbaum Institute
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHigh Performance Computing
dc.subjectSoftware
dc.subjectAI & Climate
dc.subjectSustainable AI
dc.titleIDLEWiSE. A Project Concept for AI-Assisted Energy Efficiency in HPC Clusters
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleWeizenbaum Journal of the Digital Society
dcterms.bibliographicCitation.originalpublisherplaceBerlin
dcterms.bibliographicCitation.volume5
local.series.nameWeizenbaum Journal of the Digital Society
Dateien
Originalbündel
Gerade angezeigt 1 - 1 von 1
Lade...
Vorschaubild
Name:
WJDS_5_1_5_Fusar+Bassini_et_al.pdf
Größe:
515.02 KB
Format:
Adobe Portable Document Format
Beschreibung: