Prediction and Characterization of Application Power Use in a High-Performance Computing Environment

Bruce Bugbee, Caleb Phillips, Kenny Gruchalla, Avi Purkayastha, Hilary Egan, Ryan Elmore

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

Abstract

Power use in data centers and high-performance computing (HPC) facilities has grown in tandem with increases in the size and number of these facilities. Substantial innovation is needed to enable meaningful reduction in energy footprints in leadership-class HPC systems. In this paper, we focus on characterizing and investigating application-level power usage. We demonstrate potential methods for predicting power usage based on a priori and in situ characteristics. Finally, we highlight a potential use case of this method through a simulated power-aware scheduler using historical jobs from a real scientific HPC system.

Original languageAmerican English
Pages (from-to)155-165
Number of pages11
JournalStatistical Analysis and Data Mining
Volume10
Issue number3
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 Wiley Periodicals, Inc.

NREL Publication Number

  • NREL/JA-2C00-67863

Keywords

  • HPC
  • queueing systems
  • renewable energy
  • scientific computing

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