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Metrics for energy-aware software optimisation
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Roberts, Stephen I., Wright, Steven A., Fahmy, Suhaib A. and Jarvis, Stephen A. (2017) Metrics for energy-aware software optimisation. In: Kunkel, J. and Yokota , R. and Balaji , P. and Keyes , D., (eds.) High Performance Computing 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18–22, 2017, Proceedings. Lecture Notes in Computer Science, 10266 . Springer, pp. 413-430. ISBN 9783319586663
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Official URL: https://doi.org/10.1007/978-3-319-58667-0_22
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 ( EtnEtn ) family, are unsuitable for energy-aware 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 EtnEtn 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.
Item Type: | Book Item | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Engineering > Engineering |
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Library of Congress Subject Headings (LCSH): | Computer software -- Energy consumption -- Standards, Computer software -- Energy consumption -- Measurement | ||||||||
Series Name: | Lecture Notes in Computer Science | ||||||||
Journal or Publication Title: | Lecture Notes in Computer Science | ||||||||
Publisher: | Springer | ||||||||
ISBN: | 9783319586663 | ||||||||
ISSN: | 0302-9743 | ||||||||
Book Title: | High Performance Computing 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18–22, 2017, Proceedings | ||||||||
Editor: | Kunkel, J. and Yokota , R. and Balaji , P. and Keyes , D. | ||||||||
Official Date: | 17 June 2017 | ||||||||
Dates: |
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Volume: | 10266 | ||||||||
Page Range: | pp. 413-430 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 29 March 2017 | ||||||||
Date of first compliant Open Access: | 31 July 2017 | ||||||||
Funder: | Atomic Weapons Establishment (Great Britain) (AWE), Atos (Firm), Allinea (Firm) |
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