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Power Prediction for Sustainable HPC
https://nied-repo.bosai.go.jp/records/6410
https://nied-repo.bosai.go.jp/records/6410e35ea57c-e00f-4597-b21e-88d4802ab14f
| Item type | researchmap(1) | |||||||||||||||||||||||
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| 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
× Hiraoka Michiko
× Shiraishi Takashi
× Kreshpa Enxhi
× Yamamoto Takuji
× Fukuda Hiroyuki
× Matsui Shuji
× Fujisaki Masahide
× 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 |
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| 出版者 | ||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||
| 出版者 | Information Processing Society of Japan | |||||||||||||||||||||||
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| 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 | |||||||||||||||||||||||
