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  1. 防災科研関係論文

Power Prediction for Sustainable HPC

https://nied-repo.bosai.go.jp/records/6410
https://nied-repo.bosai.go.jp/records/6410
e35ea57c-e00f-4597-b21e-88d4802ab14f
Item type researchmap(1)
公開日 2023-09-20
タイトル
言語 en
タイトル Power Prediction for Sustainable HPC
言語
言語 eng
著者 Suzuki Shigeto

× Suzuki Shigeto

en Suzuki Shigeto

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Hiraoka Michiko

× Hiraoka Michiko

en Hiraoka Michiko

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Shiraishi Takashi

× Shiraishi Takashi

en Shiraishi Takashi

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Kreshpa Enxhi

× Kreshpa Enxhi

en Kreshpa Enxhi

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Yamamoto Takuji

× Yamamoto Takuji

en Yamamoto Takuji

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Fukuda Hiroyuki

× Fukuda Hiroyuki

en Fukuda Hiroyuki

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Matsui Shuji

× Matsui Shuji

en Matsui Shuji

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Fujisaki Masahide

× Fujisaki Masahide

en Fujisaki Masahide

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Uno Atsuya

× Uno Atsuya

en Uno Atsuya

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抄録
内容記述タイプ Other
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.
言語 en
書誌情報 en : Journal of Information Processing

巻 14, 号 1, p. 283-294, 発行日 2021-02-17
出版者
言語 en
出版者 Information Processing Society of Japan
ISSN
収録物識別子タイプ EISSN
収録物識別子 1882-6652
DOI
関連識別子 10.2197/ipsjjip.29.283
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