research-article

A measurement study of GPU DVFS on energy conservation

Authors:

Ling Sing Yung,

Xiaowen ChuAuthors Info & Claims

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

Article No.: 10, Pages 1 - 5

https://doi.org/10.1145/2525526.2525852

Published: 03 November 2013 Publication History

Abstract

Nowadays, GPUs are widely used to accelerate many high performance computing applications. Energy conservation of such computing systems has become an important research topic. Dynamic voltage/frequency scaling (DVFS) is proved to be an appealing method for saving energy for traditional computing centers. However, there is still a lack of firsthand study on the effectiveness of GPU DVFS. This paper presents a thorough measurement study that aims to explore how GPU DVFS affects the system energy consumption. We conduct experiments on a real GPU platform with 37 benchmark applications. Our results show that GPU voltage/frequency scaling is an effective approach to conserving energy. For example, by scaling down the GPU core voltage and frequency, we have achieved an average of 19.28% energy reduction compared with the default setting, while giving up no more than 4% of performance. For all tested GPU applications, core voltage scaling is significantly effective to reduce system energy consumption. Meanwhile the effects of scaling core frequency and memory frequency depend on the characteristics of GPU applications.

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Cited By

Dobkin DCever NLevi I(2025)RAD-FS: Remote Timing and Power SCA Security in DVFS-augmented Ultra-Low-Power Embedded SystemsACM Transactions on Embedded Computing Systems10.1145/371183624:2(1-27)Online publication date: 27-Jan-2025
https://dl.acm.org/doi/10.1145/3711836
Cunha SSilva LSaraiva JFernandes JLaemmel RPereira JMosses P(2024)Trading Runtime for Energy Efficiency: Leveraging Power Caps to Save Energy across Programming LanguagesProceedings of the 17th ACM SIGPLAN International Conference on Software Language Engineering10.1145/3687997.3695638(130-142)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1145/3687997.3695638
Zhang YWang QLin ZXu PWang B(2024)Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance AssuranceProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3629584(769-785)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3629584
Show More Cited By

Index Terms

A measurement study of GPU DVFS on energy conservation
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics

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cover image ACM Conferences

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

November 2013

66 pages

ISBN:9781450324588

DOI:10.1145/2525526

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 03 November 2013

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Research Grants Council, University Grants Committee, Hong Kong

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SOSP '13

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SOSP '13: ACM SIGOPS 24th Symposium on Operating Systems Principles

November 3 - 6, 2013

Pennsylvania, Farmington

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HotPower '13 Paper Acceptance Rate 13 of 38 submissions, 34%;

Overall Acceptance Rate 20 of 50 submissions, 40%

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Cited By

Dobkin DCever NLevi I(2025)RAD-FS: Remote Timing and Power SCA Security in DVFS-augmented Ultra-Low-Power Embedded SystemsACM Transactions on Embedded Computing Systems10.1145/371183624:2(1-27)Online publication date: 27-Jan-2025
https://dl.acm.org/doi/10.1145/3711836
Cunha SSilva LSaraiva JFernandes JLaemmel RPereira JMosses P(2024)Trading Runtime for Energy Efficiency: Leveraging Power Caps to Save Energy across Programming LanguagesProceedings of the 17th ACM SIGPLAN International Conference on Software Language Engineering10.1145/3687997.3695638(130-142)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1145/3687997.3695638
Zhang YWang QLin ZXu PWang B(2024)Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance AssuranceProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3629584(769-785)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3629584
Wang FHao MZhang WWang Z(2024)Model-Free GPU Online Energy OptimizationIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33149169:2(141-154)Online publication date: Mar-2024
https://doi.org/10.1109/TSUSC.2023.3314916
Simsek OPiccinali JCiorba F(2024)Increasing Energy Efficiency of Astrophysics Simulations Through GPU Frequency ScalingProceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis10.1109/SCW63240.2024.00229(1826-1834)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SCW63240.2024.00229
Wang QLi LLuo WZhang YWang B(2024)DSO: A GPU Energy Efficiency Optimizer by Fusing Dynamic and Static Information2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682917(1-6)Online publication date: 19-Jun-2024
https://doi.org/10.1109/IWQoS61813.2024.10682917
Natori KOtani IHarasawa HSaito SFujimoto K(2024)Power Saving for Hardware Accelerated Applications With Dynamical Processor SwitchingIEEE Access10.1109/ACCESS.2024.344843212(118109-118121)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3448432
Gralka PMüller CHeinemann MReina GWeiskopf DErtl T(2024)Power overwhelming: the one with the oscilloscopesJournal of Visualization10.1007/s12650-024-01001-027:6(1171-1193)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s12650-024-01001-0
Bharadwaj SDas SMazumdar KBeckmann BKosonocky SAamodt TSwift MJerger N(2023)Predict; Don't React for Enabling Efficient Fine-Grain DVFS in GPUsProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 410.1145/3623278.3624756(253-267)Online publication date: 25-Mar-2023
https://dl.acm.org/doi/10.1145/3623278.3624756
Zamani HBhuyan LChen JChen Z(2023)GreenMD: Energy-efficient Matrix Decomposition on Heterogeneous Multi-GPU SystemsACM Transactions on Parallel Computing10.1145/358359010:2(1-23)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3583590
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