{"_buckets": {"deposit": "da6394b4-b62b-4fc2-b918-c1fa5a5d51e0"}, "_deposit": {"created_by": 7, "id": "6410", "owners": [7], "pid": {"revision_id": 0, "type": "depid", "value": "6410"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00006410", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2021-02-17", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "1", "bibliographicPageEnd": "294", "bibliographicPageStart": "283", "bibliographicVolumeNumber": "14", "bibliographic_titles": [{"bibliographic_title": "Journal of Information Processing", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "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.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Information Processing Society of Japan", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.2197/ipsjjip.29.283"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1882-6652", "subitem_source_identifier_type": "EISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Suzuki Shigeto", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Hiraoka Michiko", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shiraishi Takashi", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Kreshpa Enxhi", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yamamoto Takuji", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Fukuda Hiroyuki", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Matsui Shuji", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Fujisaki Masahide", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Uno Atsuya", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_title": "Power Prediction for Sustainable HPC", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Power Prediction for Sustainable HPC", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "7", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/6410", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-09-20"}, "publish_date": "2023-09-20", "publish_status": "0", "recid": "6410", "relation": {}, "relation_version_is_last": true, "title": ["Power Prediction for Sustainable HPC"], "weko_shared_id": -1}