Metrics for energy-aware software optimisation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • University of Warwick

Abstract

Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community are often re-purposed to guide these software optimisation efforts. In this paper we argue that established metrics, and in particular those in the Energy Delay Product (Etn) family, are unsuitable for energyaware software optimisation. A good metric should provide meaningful values for a single experiment, allow fair comparison between experiments, and drive optimisation in a sensible direction. We show that Etn metrics are unable to fulfil these basic requirements and present suitable alternatives for guiding energy-aware software optimisation. We finish with a practical demonstration of the utility of our proposed metrics.

Bibliographic note

Funding Information: The authors would like to thank Thomas Ilsche and the Center of Information Services and High Performance Computing (ZIH) at TU Dresden. This research is funded in part by research grants from AWE, ATOS and Allinea. Professor Stephen Jarvis is an AWE William Penney Fellow. Publisher Copyright: © Springer International Publishing AG 2017.

Details

Original languageEnglish
Title of host publicationHigh Performance Computing - 32nd International Conference, ISC High Performance 2017, Proceedings
EditorsJulian M. Kunkel, Pavan Balaji, David Keyes, Rio Yokota
Publication statusPublished - 2017
Event32nd International Conference, ISC High Performance, 2017 - Frankfurt, Germany
Duration: 18 Jun 201722 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10266 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd International Conference, ISC High Performance, 2017
Country/TerritoryGermany
CityFrankfurt
Period18/06/1722/06/17