Prediction and characterization of application power use in a high-performance computing environment

Journal Article · Mon Feb 27 00:00:00 EST 2017 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11339· OSTI ID:1361232

Bugbee, Bruce ^[1]; Phillips, Caleb ^[1]; Egan, Hilary ^[2]; Elmore, Ryan ^[3]; Gruchalla, Kenny ^[1]; Purkayastha, Avi ^[1]

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1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
2. Univ. of Colorado, Boulder, CO (United States)
3. Univ. of Denver, Denver, CO (United States)

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• Grant/Contract Number: AC36-08GO28308; DE‐AC36‐08GO28308
• OSTI ID: 1361232
• Alternate ID(s): OSTI ID: 1400847
• Report Number(s): NREL/JA-2C00-67863
• Journal Information: Statistical Analysis and Data Mining, Vol. 10, Issue 3; ISSN 1932-1864
• Publisher: Wiley
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
HPC
queueing systems
renewable energy
scientific computing