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Tsinghua Science and Technology 2021, 26(3): 370-383 https://doi.org/10.26599/TST.2020.9010012
Open Access | Issue | Published: 12 October 2020

More Bang for Your Buck: Boosting Performance with Capped Power Consumption

Show Author's Information Juan Chen( ),  Xinxin Qi,  Feihao Wu,  Jianbin Fang,  Yong Dong,  Yuan Yuan,  Zheng Wang,  Keqin Li
College of Computer, National University of Defense Technology, Changsha 410073, China.
College of Computer, University of Leeds, London LS2 9JT, UK.
School of Science and Engineering, State University of New York, New York, NY 12561, USA.
Keywords:
energy efficiency, high-performance computing, performance boost, power control, processor frequency scaling
Cite this article:
Chen J, Qi X, Wu F, et al. More Bang for Your Buck: Boosting Performance with Capped Power Consumption. Tsinghua Science and Technology, 2021, 26(3): 370-383. https://doi.org/10.26599/TST.2020.9010012
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Abstract Full text About this article

Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge. This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing (HPC) clusters, aiming to improve application performance without breaching peak power constraints and total energy consumption. Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance. It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so. We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge (HPCC) benchmark suites and evaluate it on a representative HPC cluster. Experimental results show that our approach can effectively mitigate memory contention to improve application performance, and it achieves this without significantly increasing the peak power and overall energy consumption. Our approach obtains on average 12.69% performance improvement over the default resource allocation strategy, but uses 7.06% less total power, which translates into 17.77% energy savings.


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About this article

More Bang for Your Buck: Boosting Performance with Capped Power Consumption

Show Author's information Juan Chen( ), Xinxin Qi, Feihao Wu, Jianbin Fang, Yong Dong, Yuan Yuan, Zheng Wang, Keqin Li
College of Computer, National University of Defense Technology, Changsha 410073, China.
College of Computer, University of Leeds, London LS2 9JT, UK.
School of Science and Engineering, State University of New York, New York, NY 12561, USA.

Abstract

Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge. This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing (HPC) clusters, aiming to improve application performance without breaching peak power constraints and total energy consumption. Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance. It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so. We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge (HPCC) benchmark suites and evaluate it on a representative HPC cluster. Experimental results show that our approach can effectively mitigate memory contention to improve application performance, and it achieves this without significantly increasing the peak power and overall energy consumption. Our approach obtains on average 12.69% performance improvement over the default resource allocation strategy, but uses 7.06% less total power, which translates into 17.77% energy savings.

Keywords: energy efficiency, high-performance computing, performance boost, power control, processor frequency scaling

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Publication history

Received: 25 March 2020
Accepted: 02 April 2020
Published: 12 October 2020
Issue date: June 2021

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© The author(s) 2021.

Acknowledgements

This work was supported in part by the Advanced Research Project of China (No. 31511010203) and the Research Program of NUDT (No. ZK18-03-10).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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