Evaluating the impacts of code-level performance tunings on power efficiency

Satoshi Imamura, Keitaro Oka, Yuichiro Yasui, Yuichi Inadomi, Katsuki Fujisawa, Toshio Endo, Koji Ueno, Keiichiro Fukazawa, Nozomi Hata, Yuta Kakibuka, Koji Inoue, Takatsugu Ono

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

As the power consumption of HPC systems will be a primary constraint for exascale computing, a main objective in HPC communities is recently becoming to maximize power efficiency (i.e., performance per watt) rather than performance. Although programmers have spent a considerable effort to improve performance by tuning HPC programs at a code level, tunings for improving power efficiency is now required. In this work, we select two representative HPC programs (Graph500 and SDPARA) and evaluate how traditional code-level performance tunings applied to these programs affect power efficiency. We also investigate the impacts of the tunings on power efficiency at various operating frequencies of CPUs and/or GPUs. The results show that the tunings significantly improve power efficiency, and different types of tunings exhibit different trends in power efficiency by varying CPU frequency. Finally, the scalability and power efficiency of state-of-the-art Graph500 implementations are explored on both a single-node platform and a 960-node supercomputer. With their high scalability, they achieve 27.43 MTEPS/Watt with 129.76 GTEPS on the single-node system and 4.39 MTEPS/Watt with 1,085.24 GTEPS on the supercomputer.

本文言語英語
ホスト出版物のタイトルProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
編集者Ronay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
出版社Institute of Electrical and Electronics Engineers Inc.
ページ362-369
ページ数8
ISBN(電子版)9781467390040
DOI
出版ステータス出版済み - 1 1 2016
イベント4th IEEE International Conference on Big Data, Big Data 2016 - Washington, 米国
継続期間: 12 5 201612 8 2016

出版物シリーズ

名前Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

その他

その他4th IEEE International Conference on Big Data, Big Data 2016
国/地域米国
CityWashington
Period12/5/1612/8/16

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • ハードウェアとアーキテクチャ

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