The impact of GPU DVFS on the energy and performance of deep Learning: An Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang WANG, Xiaowen CHU

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Over the past years, great progress has been made in improving the computing power of general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of deep neural networks (DNNs) in multiple fields like computer vision and natural language processing. A typical DNN training process repeatedly updates tens of millions of parameters, which not only requires huge computing resources but also consumes significant energy. In order to train DNNs in a more energy-efficient way, we empirically investigate the impact of GPU Dynamic Voltage and Frequency Scaling (DVFS) on the energy consumption and performance of deep learning. Our experiments cover a wide range of GPU architectures, DVFS settings, and DNN configurations. We observe that, compared to the default core frequency settings of three tested GPUs, the optimal core frequency can help conserve 8.7%~23.1% energy consumption for different DNN training cases. Regarding the inference, the benefits vary from 19.6%~26.4%. Our findings suggest that GPU DVFS has great potentials to help develop energy efficient DNN training/inference schemes.

Original languageEnglish
Title of host publicatione-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery, Inc
Pages315-325
Number of pages11
ISBN (Electronic)9781450366717
DOIs
Publication statusPublished - 15 Jun 2019
Event10th ACM International Conference on Future Energy Systems, e-Energy 2019 - Phoenix, United States
Duration: 25 Jun 201928 Jun 2019

Publication series

Namee-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems

Conference

Conference10th ACM International Conference on Future Energy Systems, e-Energy 2019
Country/TerritoryUnited States
CityPhoenix
Period25/06/1928/06/19

Scopus Subject Areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology

User-Defined Keywords

  • Deep Convolutional Neural Network
  • Dynamic Voltage and Frequency Scaling
  • Graphics Processing Units

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