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perun: Benchmarking Energy Consumption of High-Performance Computing Applications

Gutiérrez Hermosillo Muriedas, Juan Pedro 1; Flügel, Katharina 1; Debus, Charlotte 1; Obermaier, Holger 1; Streit, Achim ORCID iD icon 1; Götz, Markus ORCID iD icon 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

Looking closely at the Top500 list of high-performance computers (HPC) in the world, it becomes clear that computing power is not the only number that has been growing in the last three decades. The amount of power required to operate such massive computing machines has been steadily increasing, earning HPC users a higher than usual carbon footprint. While the problem is well known in academia, the exact energy requirements of hardware, software and how to optimize it are hard to quantify. To tackle this issue, we need tools to understand the software and its relationship with power consumption in today’s high performance computers. With that in mind, we present perun, a Python package and command line interface to measure energy consumption based on hardware performance counters and selected physical measurement sensors. This enables accurate energy measurements on various scales of computing, from a single laptop to an MPI-distributed HPC application. We include an analysis of the discrepancies between these sensor readings and hardware performance counters, with particular focus on the power draw of the usually overlooked non-compute components such as memory. ... mehr


Originalveröffentlichung
DOI: 10.1007/978-3-031-39698-4_2
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Zitationen: 2
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2023
Sprache Englisch
Identifikator ISBN: 978-3-031-39698-4
ISSN: 0302-9743
KITopen-ID: 1000172675
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 Euro-Par 2023: Parallel Processing. Ed.: J. Cano
Veranstaltung 29th International European Conference on Parallel and Distributed Computing (Euro-Par 2023), Limassol, Zypern, 28.08.2023 – 01.09.2023
Verlag Springer Nature Switzerland
Seiten 17–31
Serie Lecture Notes in Computer Science ; 14100
Vorab online veröffentlicht am 24.08.2023
Schlagwörter Energy Benchmarking, High-performance Computing, Artificial Intelligence, Distributed Memory System
Nachgewiesen in Scopus
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