A survey on software methods to improve the energy efficiency of parallel computing. (November 2017)

• Record Type: Journal Article
• Authors: Jin, Chao; de Supinski, Bronis R; Abramson, David; Poxon, Heidi; DeRose, Luiz; Dinh, Minh Ngoc; Endrei, Mark; Jessup, Elizabeth R
• Is Part Of: International journal of high performance computing applications. Volume 31:Number 6(2017)
• Page Start: 517
• Page End: 549
• Publication Date: 2017-11
• Subjects: Parallel computing -- high performance computing -- power saving -- energy efficiency -- auto-tuning; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342016665471
• Languages: English
• ISSNs: 1094-3420

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.