Chen, J, Qi, X, Wu, F et al. (5 more authors) (2021) More bang for your buck: Boosting performance with capped power consumption. Tsinghua Science and Technology, 26 (3). pp. 370-383. ISSN 1007-0214
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The author(s) 2021. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | energy efficiency , high-performance computing , performance boost , power control , processor frequency scaling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Nov 2020 16:14 |
Last Modified: | 25 Jun 2023 22:30 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.26599/tst.2020.9010012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168415 |