Prediction and characterization of application power use in a high‐performance computing environment. (27th February 2017)

• Record Type: Journal Article
• Authors: Bugbee, Bruce; Phillips, Caleb; Egan, Hilary; Elmore, Ryan; Gruchalla, Kenny; Purkayastha, Avi
• Is Part Of: Statistical analysis and data mining. Volume 10:Number 3(2017)
• Page Start: 155
• Page End: 165
• Publication Date: 2017-02-27
• Subjects: HPC -- queueing systems -- renewable energy -- scientific computing; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11339
• Languages: English
• ISSNs: 1932-1864

Abstract

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.