Power Prediction for Sustainable HPC

• Item type: Trans(1)
• 公開日: 2021-02-17
• タイトル: Power Prediction for Sustainable HPC
• タイトル / 言語: en
• タイトル: Power Prediction for Sustainable HPC
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: power prediction, HPC, deep learning, job scheduling
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Fujitsu Laboratories LTD.
• 著者所属: Fujitsu LTD.
• 著者所属: Fujitsu Laboratories LTD.
• 著者所属: Fujitsu Laboratories LTD.
• 著者所属: Fujitsu Laboratories LTD.
• 著者所属: Fujitsu Laboratories LTD.
• 著者所属: Fujitsu LTD.
• 著者所属: Fujitsu LTD.
• 著者所属: RIKEN Center for Computational Science
• 著者所属(英): en
• 著者所属(英): Fujitsu Laboratories LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu Laboratories LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu Laboratories LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu Laboratories LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu Laboratories LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu LTD.
• 著者所属(英): en
• 著者所属(英): Fujitsu LTD.
• 著者所属(英): en
• 著者所属(英): RIKEN Center for Computational Science
• 著者名: Shigeto, Suzuki; Michiko, Hiraoka; Takashi, Shiraishi; Enxhi, Kreshpa; Takuji, Yamamoto; Hiroyuki, Fukuda; Shuji, Matsui; Masahide, Fujisaki; Atsuya, Uno
• 著者名(英): Shigeto, Suzuki; Michiko, Hiraoka; Takashi, Shiraishi; Enxhi, Kreshpa; Takuji, Yamamoto; Hiroyuki, Fukuda; Shuji, Matsui; Masahide, Fujisaki; Atsuya, Uno
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Exascale computers consume huge amounts of power and their variation over time makes system energy management important. Because of time lag in cooling-units operation, predictive control is desirable for effective power control. In this work, we report a state-of-the-art power prediction model. Conventional methods with topic model use the power of past job as a prediction based on the similarity of job information. The prediction, however, fails, if there is no correct data before. To resolve this, we developed a recurrent neural network model with variable network size, which detects features of power shape from its power history and enables precise prediction during job execution. By integrating these models into a single algorithm, the optimal model is automatically adopted for prediction according to the job status. We demonstrated high-precision prediction with an average relative error of 5.7% in K computer as compared to that of 20.1% by the conventional method.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Exascale computers consume huge amounts of power and their variation over time makes system energy management important. Because of time lag in cooling-units operation, predictive control is desirable for effective power control. In this work, we report a state-of-the-art power prediction model. Conventional methods with topic model use the power of past job as a prediction based on the similarity of job information. The prediction, however, fails, if there is no correct data before. To resolve this, we developed a recurrent neural network model with variable network size, which detects features of power shape from its power history and enables precise prediction during job execution. By integrating these models into a single algorithm, the optimal model is automatically adopted for prediction according to the job status. We demonstrated high-precision prediction with an average relative error of 5.7% in K computer as compared to that of 20.1% by the conventional method.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11833852
• 書誌情報: 情報処理学会論文誌コンピューティングシステム（ACS）; 巻 14, 号 1, 発行日 2021-02-17
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7829
• 出版者 / 言語: ja
• 出版者: 情報処理学会