A survey on software methods to improve the energy efficiency of parallel computing

Jin, Chao, de Supinski, Bronis R., Abramson, David, Poxon, Heidi, DeRose, Luiz, Dinh, Minh Ngoc, Endrei, Mark and Jessup, Elizabeth R. (2016) A survey on software methods to improve the energy efficiency of parallel computing. International Journal of High Performance Computing Applications, . doi:10.1177/1094342016665471


Author Jin, Chao
de Supinski, Bronis R.
Abramson, David
Poxon, Heidi
DeRose, Luiz
Dinh, Minh Ngoc
Endrei, Mark
Jessup, Elizabeth R.
Title A survey on software methods to improve the energy efficiency of parallel computing
Journal name International Journal of High Performance Computing Applications   Check publisher's open access policy
ISSN 1094-3420
1741-2846
Publication date 2016-09-09
Sub-type Article (original research)
DOI 10.1177/1094342016665471
Open Access Status Not yet assessed
Total pages 33
Place of publication London, United Kingdom
Publisher Sage Publications
Collection year 2017
Language eng
Abstract Energy consumption is one of the top challenges for achieving the next generation of supercomputing. Codesign of hardware and software is critical for improving energy efficiency (EE) for future large-scale systems. Many architectural power-saving techniques have been developed, and most hardware components are approaching physical limits. Accordingly, parallel computing software, including both applications and systems, should exploit power-saving hardware innovations and manage efficient energy use. In addition, new power-aware parallel computing methods are essential to decrease energy usage further. This article surveys software-based methods that aim to improve EE for parallel computing. It reviews the methods that exploit the characteristics of parallel scientific applications, including load imbalance and mixed precision of floating-point (FP) calculations, to improve EE. In addition, this article summarizes widely used methods to improve power usage at different granularities, such as the whole system and per application. In particular, it describes the most important techniques to measure and to achieve energy-efficient usage of various parallel computing facilities, including processors, memories, and networks. Overall, this article reviews the state-of-the-art of energy-efficient methods for parallel computing to motivate researchers to achieve optimal parallel computing under a power budget constraint.
Keyword Parallel computing
High performance computing
Power saving
Energy efficiency
Auto-tuning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Fri, 26 May 2017, 11:21:10 EST by Ms Diana Cassidy on behalf of School of Information Technol and Elec Engineering