Free access to special issue paper paper on prediction of application power use

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  • Author: Statistics Views
  • Date: 13 November 2017

Each week, we select a recently published article and provide free access. This week's is from Statistical Analysis and Data Mining: The ASA Data Science Journal and is available from the June 2017 issue.

To read the article in full, please click the link below:

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

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

Statistical Analysis and Data Mining: The ASA Data Science Journal

Special Issue: CoDA 2016 Special Issue: Selected Papers from the Conference on Data Analysis 2016 – Part I

Volume 10, Issue 3, pages 155–165, June 2017
DOI: 10.1002/sam.11339

The introduction is provided below:

thumbnail image: Free access to special issue paper paper on prediction of application power use

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.

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