{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229565","sets":["6504:11436:11437"]},"path":["11437"],"owner":"44499","recid":"229565","title":["畳み込みニューラルネットワークにおける分割モデルのGPUへの割り当て"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"5618db43-d9fc-4d8f-a780-059db6d0e275"},"_deposit":{"id":"229565","pid":{"type":"depid","value":"229565","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"畳み込みニューラルネットワークにおける分割モデルのGPUへの割り当て","author_link":["617492","617491"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"畳み込みニューラルネットワークにおける分割モデルのGPUへの割り当て"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータシステム","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"明大"},{"subitem_text_value":"明大"}]},"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/229565/files/IPSJ-Z85-5J-01.pdf","label":"IPSJ-Z85-5J-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5J-01.pdf","filesize":[{"value":"125.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"fc906116-9a1f-4906-9850-b6459ec56733","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"綿貫, 幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"吉田, 明正"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"画像認識をはじめとする多分野で活用される深層学習は,精度向上のために大量のデータによる学習やモデルの大規模化が必要とされ,学習時間の長時間化が課題となる.高い並列処理性能を持つGPUは学習の高速化に活用されており,マルチGPUを用いた効率的な並列処理を実現する手法として,学習モデルを分割してGPUに割り当てるモデル並列のアプローチがある.本研究では,代表的な深層学習手法であるCNNに対して,各GPUに複数ステージを割り当てたモデル並列を適用し,マルチGPU環境での高速化を図る.画像分類CNNのマルチGPU向け並列プログラムをCUDAとOpenMPを用いて実装し,NVIDIA Tesla K80搭載サーバ上で性能評価を行い,有効性を確認した.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"66","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"65","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:28:47.947214+00:00","updated":"2025-01-19T11:30:36.332435+00:00","id":229565}