Work

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

Year: 2017

Type: article

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.

Source: Statistical Analysis and Data Mining The ASA Data Science Journal

DOI: 10.1002/sam.11339

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

Institutions: National Laboratory of the Rockies, University of Colorado Boulder, University of Denver

Language: English

Topic: Energy Efficiency in Computing

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green