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.

References

[1]

Y. Abe, H. Sasaki, M. Peres, K. Inoue, K. Murakami, and S. Kato. Power and performance analysis of gpu-accelerated systems. In HotPower12. ACM, 2012.

Digital Library

[2]

A. Bakhoda, G. L. Yuan, W. W. Fung, H. Wong, and T. M. Aamodt. Analyzing cuda workloads using a detailed gpu simulator. In Performance Analysis of Systems and Software, 2009. ISPASS 2009. IEEE International Symposium on, pages 163--174. IEEE, 2009.

[3]

S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S.-H. Lee, and K. Skadron. Rodinia: A benchmark suite for heterogeneous computing. In Workload Characterization, 2009. IISWC 2009. IEEE International Symposium on, pages 44--54. IEEE, 2009.

Digital Library

[4]

X. Chu and K. Zhao. Practical random linear network coding on gpus. In GPU Solutions to Multi-scale Problems in Science and Engineering, pages 115--130. Springer, 2013.

[5]

X. Chu, K. Zhao, and M. Wang. Massively parallel network coding on gpus. In Performance, Computing and Communications Conference, 2008. IPCCC 2008. IEEE International, pages 144--151. IEEE, 2008.

[6]

H. David, C. Fallin, E. Gorbatov, U. R. Hanebutte, and O. Mutlu. Memory power management via dynamic voltage/frequency scaling. In 8th ACM international conference on Autonomic computing, pages 31--40. ACM, 2011.

Digital Library

[7]

M. Etinski, J. Corbalan, J. Labarta, and M. Valero. Understanding the future of energy-performance trade-off via dvfs in hpc environments. Journal of Parallel and Distributed Computing, 72(4): 579--590, 2012.

Digital Library

[8]

R. Ge, R. Vogt, J. Majumder, A. Alam, M. Burtscher, and Z. Zong. Effects of dynamic voltage and frequency scaling on a k20 gpu. In 2nd International Workshop on Power-aware Algorithms, Systems, and Architectures. IEEE, 2013.

Digital Library

[9]

S. Hong. Modeling performance and power for energy-efficient gpgpu computing. PhD thesis, Georgia Institute of Technology, 2012.

[10]

S. Hong and H. Kim. An analytical model for a gpu architecture with memory-level and thread-level parallelism awareness. In ACM SIGARCH Computer Architecture News, volume 37, pages 152--163. ACM, 2009.

Digital Library

[11]

S. Hong and H. Kim. An integrated gpu power and performance model. In ACM SIGARCH Computer Architecture News, volume 38, pages 280--289. ACM, 2010.

Digital Library

[12]

Y. Jiao, H. Lin, P. Balaji, and W. Feng. Power and performance characterization of computational kernels on the gpu. In Green Computing and Communications (Green-Com), 2010 IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom), pages 221--228. IEEE, 2010.

Digital Library

[13]

J. Lee and N. S. Kim. Optimizing total power of many-core processors considering voltage scaling limit and process variations. In 14th ACM/IEEE international symposium on Low power electronics and design, pages 201--206. ACM, 2009.

Digital Library

[14]

J. Lee, V. Sathisha, M. Schulte, K. Compton, and N. S. Kim. Improving throughput of power-constrained gpus using dynamic voltage/frequency and core scaling. In Parallel Architectures and Compilation Techniques (PACT), 2011 International Conference on, pages 111--120. IEEE, 2011.

Digital Library

[15]

J. Leng, T. Hetherington, A. ElTantawy, S. Gilani, N. S. Kim, T. M. Aamodt, and V. J. Reddi. Gpuwattch: Enabling energy optimizations in gpgpus. In ISCA, volume 40, 2013.

Digital Library

[16]

Y. Li, K. Zhao, X. Chu, and J. Liu. Speeding up k-means algorithm by gpus. Journal of Computer and System Sciences, 79(2): 216--229, 2013.

Digital Library

[17]

M. Y. Lim and V. W. Freeh. Determining the minimum energy consumption using dynamic voltage and frequency scaling. In Parallel and Distributed Processing Symposium, pages 1--8. IEEE, 2007.

[18]

MSI. Afterburner, graphics card performance booster. http://event.msi.com/vga/afterburner/download.htm.

[19]

H. Nagasaka, N. Maruyama, A. Nukada, T. Endo, and S. Matsuoka. Statistical power modeling of gpu kernels using performance counters. In Green Computing Conference, 2010 International, pages 115--122. IEEE, 2010.

Digital Library

[20]

NVIDIA. Gpu computing sdk. https://developer.nvidia.com/gpu-computing-sdk.

[21]

Orbmu2k. Nvidia inspector. http://blog.orbmu2k.de/tools/nvidia-inspector-tool.

[22]

TechPowerUp. Gpu-z. http://www.techpowerup.com/gpuz/.

Cited By

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
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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SIGOPS

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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ACM SIGOPS 31st Symposium on Operating Systems Principles

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Cited By

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
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
Tröpgen HBielert MIlsche TVieira MCardellini VDi Marco ATuma P(2023)Evaluating the Energy Measurements of the IBM POWER9 On-Chip ControllerProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583729(67-76)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578244.3583729
Yu JKim JSeo E(2023)Know Your Enemy To Save Cloud Energy: Energy-Performance Characterization of Machine Learning Serving2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA56546.2023.10070943(842-854)Online publication date: Feb-2023
https://doi.org/10.1109/HPCA56546.2023.10070943
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