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Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

Caspart, René ORCID iD icon 1; Ziegler, Sebastian 2; Weyrauch, Arvid 1; Obermaier, Holger 1; Raffeiner, Simon 1; Schuhmacher, Leon Pascal 1; Scholtyssek, Jan 2; Trofimova, Darya 2; Nolden, Marco 2; Reinartz, Ines 3; Isensee, Fabian 2; Götz, Markus 1; Debus, Charlotte 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)
2 Helmholtz-Gemeinschaft Deutscher Forschungszentren (DKFZ)
3 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)


With the rise of artificial intelligence (AI) in recent years and the subsequent increase in complexity of the applied models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly more potent accelerator hardware as well as the use of large and powerful compute clusters. However, the gain in prediction accuracy from large models trained on distributed and accelerated systems ultimately comes at the price of a substantial increase in energy demand, and researchers have started questioning the environmental friendliness of such AI methods at scale. Consequently, awareness of energy efficiency plays an important role for AI model developers and hardware infrastructure operators likewise. The energy consumption of AI workloads depends both on the model implementation and the composition of the utilized hardware. Therefore, accurate measurements of the power draw of respective AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware. Towards this end, we present measurements of the energy consumption of two typical applications of deep learning models on different types of heterogeneous compute nodes. ... mehr

DOI: 10.1007/978-3-031-23220-6_8
Zitationen: 3
Zitationen: 4
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Scientific Computing Center (SCC)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 04.01.2023
Sprache Englisch
Identifikator ISBN: 978-3-031-23220-6
ISSN: 0302-9743
KITopen-ID: 1000154359
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Weitere HGF-Programme 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in High Performance Computing. ISC High Performance 2022 International Workshops – Hamburg, Germany, May 29 – June 2, 2022, Revised Selected Papers. Ed.: H. Anzt
Veranstaltung ICS High Performance International Workshops (2022), Hamburg, Deutschland, 29.05.2022 – 02.06.2022
Verlag Springer International Publishing
Seiten 108–121
Serie Lecture Notes in Computer Science ; 13387
Externe Relationen Abstract/Volltext
Schlagwörter Energy measurement, Artificial intelligence, Green AI, Energy efficiency, High performance computing, GPUs
Nachgewiesen in Dimensions
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