Power and performance characterization and modeling of GPU-accelerated systems

Yuki Abe, Hiroshi Sasaki, Shinpei Kato, Koji Inoue, Masato Edahiro, Martin Peres

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Citations (Scopus)

Abstract

Graphics processing units (GPUs) provide an order-of-magnitude improvement on peak performance and performance-per-watt as compared to traditional multicore CPUs. However, GPU-accelerated systems currently lack a generalized method of power and performance prediction, which prevents system designers from an ultimate goal of dynamic power and performance optimization. This is due to the fact that their power and performance characteristics are not well captured across architectures, and as a result, existing power and performance modeling approaches are only available for a limited range of particular GPUs. In this paper, we present power and performance characterization and modeling of GPU-accelerated systems across multiple generations of architectures. Characterization and modeling both play a vital role in optimization and prediction of GPU-accelerated systems. We quantify the impact of voltage and frequency scaling on each architecture with a particularly intriguing result that a cutting-edge Kepler-based GPU achieves energy saving of 75% by lowering GPU clocks in the best scenario, while Fermi- and Tesla-based GPUs achieve no greater than 40% and 13%, respectively. Considering these characteristics, we provide statistical power and performance modeling of GPU-accelerated systems simplified enough to be applicable for multiple generations of architectures. One of our findings is that even simplified statistical models are able to predict power and performance of cutting-edge GPUs within errors of 20% to 30% for any set of voltage and frequency pair.

Original languageEnglish
Title of host publicationProceedings - IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS 2014
PublisherIEEE Computer Society
Pages113-122
Number of pages10
ISBN (Print)9780769552071
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014 - Phoenix, AZ, United States
Duration: 2014 May 192014 May 23

Publication series

NameProceedings of the International Parallel and Distributed Processing Symposium, IPDPS
ISSN (Print)1530-2075
ISSN (Electronic)2332-1237

Conference

Conference28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014
CountryUnited States
CityPhoenix, AZ
Period14/5/1914/5/23

Keywords

  • characterization
  • GPUs
  • modeling
  • performance
  • power

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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  • Cite this

    Abe, Y., Sasaki, H., Kato, S., Inoue, K., Edahiro, M., & Peres, M. (2014). Power and performance characterization and modeling of GPU-accelerated systems. In Proceedings - IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS 2014 (pp. 113-122). [6877247] (Proceedings of the International Parallel and Distributed Processing Symposium, IPDPS). IEEE Computer Society. https://doi.org/10.1109/IPDPS.2014.23