{"updated":"2025-01-20T03:37:11.010579+00:00","links":{},"id":183556,"created":"2025-01-19T00:51:04.113724+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00183556","sets":["1164:2240:9116:9247"]},"path":["9247"],"owner":"11","recid":"183556","title":["A Performance Evaluation of Distributed TensorFlow"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-09-12"},"_buckets":{"deposit":"96ee914a-d9ac-4584-b707-668c39dbbc9e"},"_deposit":{"id":"183556","pid":{"type":"depid","value":"183556","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"A Performance Evaluation of Distributed TensorFlow","author_link":["403273","403272","403269","403270","403276","403275","403274","403271"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"A Performance Evaluation of Distributed TensorFlow"},{"subitem_title":"A Performance Evaluation of Distributed TensorFlow","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"分散ファイルシステムと機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2017-09-12","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大学／産業技術総合研究所"},{"subitem_text_value":"産業技術総合研究所／筑波大学"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"産業技術総合研究所／筑波大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Tsukuba / National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology / University of Tsukuba","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology / University of Tsukuba","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/183556/files/IPSJ-HPC17161001.pdf","label":"IPSJ-HPC17161001.pdf"},"date":[{"dateType":"Available","dateValue":"2019-09-12"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HPC17161001.pdf","filesize":[{"value":"2.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"14"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c0bea375-d62b-4741-bd60-5e4d2faee2e7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tianlun, Wang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Tanimura"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hirotaka, Ogawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hidemoto, Nakada"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tianlun, Wang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Tanimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hirotaka, Ogawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hidemoto, Nakada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10463942","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8841","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"TensorFlow is a deep learning framework which is developed by Google. The computations in TensorFlow are implemented and expressed as data flow graphs of multidimensional array data which is referred to as“Tensor.” We know that, with TensorFlow we can implement parallel execution in a multi-GPU configuration and distributed execution in a multi-node configuration, but it is not clear how effective it is in the real environment. In this paper, we measured these performances for several mini batch sizes and network settings. From the experimental results, we confirmed that we can accelerate the execution in all the environment we have tested. We also found that the mini batch size has a big influence in the distributed environment of 1Gbps network. However, in the environment of 10 Gbps network or the intra-node multipl-GPU configuration, we could achieve the linear speed up regardless of mini batch size.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"TensorFlow is a deep learning framework which is developed by Google. The computations in TensorFlow are implemented and expressed as data flow graphs of multidimensional array data which is referred to as“Tensor.” We know that, with TensorFlow we can implement parallel execution in a multi-GPU configuration and distributed execution in a multi-node configuration, but it is not clear how effective it is in the real environment. In this paper, we measured these performances for several mini batch sizes and network settings. From the experimental results, we confirmed that we can accelerate the execution in all the environment we have tested. We also found that the mini batch size has a big influence in the distributed environment of 1Gbps network. However, in the environment of 10 Gbps network or the intra-node multipl-GPU configuration, we could achieve the linear speed up regardless of mini batch size.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告ハイパフォーマンスコンピューティング（HPC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2017-09-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2017-HPC-161"}]},"relation_version_is_last":true,"weko_creator_id":"11"}}