{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00113246","sets":["1164:2240:7894:7895"]},"path":["7895"],"owner":"11","recid":"113246","title":["ヘテロジニアスGPUコンピューティングのためのワークサイズ自動調整手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2015-02-23"},"_buckets":{"deposit":"59f68609-f5ff-4531-a9de-382c32ecd962"},"_deposit":{"id":"113246","pid":{"type":"depid","value":"113246","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"ヘテロジニアスGPUコンピューティングのためのワークサイズ自動調整手法の提案","author_link":["37469","37468","37466","37467"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ヘテロジニアスGPUコンピューティングのためのワークサイズ自動調整手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"GPU","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2015-02-23","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学大学院情報システム学研究科"},{"subitem_text_value":"早稲田大学基幹理工学研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"電気通信大学大学院情報システム学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Systems, The University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Fundamental Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Systems, The University of Electro-Communications","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/113246/files/IPSJ-HPC15148016.pdf"},"date":[{"dateType":"Available","dateValue":"2017-02-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HPC15148016.pdf","filesize":[{"value":"548.7 kB"}],"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":"348ab4cd-7dd0-47a6-82fc-54bf214e298c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2015 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"竹本, 拓未"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"和田, 康孝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 正章"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"本多, 弘樹"}],"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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"CPU や GPU のような異なった種類の演算デバイスを混在させたヘテロジニアス構成のシステムにおいては，GPU のハードウェアアーキテクチャの特性，および実行させるプログラムの特性を把握する必要がある．その上で，GPU に割り当てる実行スレッド数や，GPU のマルチプロセッサあたりに割り当てる実行スレッド数といった様々なパラメータの値のチューニングを行わなければ，高い計算性能は得られない．さらに，異なる種類の GPU が搭載されている複数のコンピュータノードがネットワークなどを介して接続されたマルチノードなヘテロジニアス構成のシステムの場合には，それぞれの GPU に割り当てる処理の分割方法やネットワークの速度といった要素が新たに増えるため，チューニングコストが上昇してしまうと予想される．そこで本稿では，ヘテロジニアスコンピューティングのための標準フレームワークである OpenCL を対象に，GPU に割り当てるスレッド数などのワークサイズを決定する方法を提案し，評価した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","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":"2015-02-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2015-HPC-148"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":113246,"updated":"2025-01-20T19:43:13.969732+00:00","links":{},"created":"2025-01-18T23:54:52.003696+00:00"}