{"updated":"2025-01-21T20:18:50.436208+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00079294","sets":["1164:1579:6260:6627"]},"path":["6627"],"owner":"10","recid":"79294","title":["FMMを用いたペタスケール乱流解析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-11-21"},"_buckets":{"deposit":"59d5f5bd-c795-4e34-8144-7eb8d283732b"},"_deposit":{"id":"79294","pid":{"type":"depid","value":"79294","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"FMMを用いたペタスケール乱流解析","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"FMMを用いたペタスケール乱流解析"},{"subitem_title":"Petascale Turbulence Simulation Using FMM","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"GPU最適化","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2011-11-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"KAUST"},{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"ボストン大学"},{"subitem_text_value":"慶應義塾大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"King Abdullah University of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"Boston University","subitem_text_language":"en"},{"subitem_text_value":"Keio University","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/79294/files/IPSJ-ARC11197029.pdf"},"date":[{"dateType":"Available","dateValue":"2013-11-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC11197029.pdf","filesize":[{"value":"1.8 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":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f65bde3e-c719-4608-a5de-1800a3d91baa","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2011 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"横田, 理央"},{"creatorName":"成見, 哲"},{"creatorName":"L.A.Barba"},{"creatorName":"泰岡, 顕治"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rio, Yokota","creatorNameLang":"en"},{"creatorName":"Tetsu, Narumi","creatorNameLang":"en"},{"creatorName":"Lorena, Barba","creatorNameLang":"en"},{"creatorName":"Kenji, Yasuoka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096105","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":"Fast Multipole Method (FMM) は従来粒子のN体問題の高速化手法として発展してきたが，近年その応用の幅を広げる研究が多くなされている．本研究では，大規模 GPU システム向けに開発された FMM を用いて 20483 規模の乱流解析を行い，同様の計算条件のもとでスペクトル法との比較を行った．ただし，今回の解析に用いた手法は Treecode と FMM の長所を組み合わせたハイブリッド型になっており，GPU 上で高い Flops が出る treecode の特長をさらに高速なアルゴリズムである FMM で実現している．TSUBAME2.0 上で 4096 GPU を用いた計算において 74% の並列化効率を得た．また，このときの演算性能は 1.01PFlops であった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Fast multipole methods (FMM) were originally developed for accelerating N-body problems in astrophysics and other particle based methods. A recent trend in HPC has been to use FMMs in unconventional application areas. We have performed a 20483 turbulence calculation using an FMM designed for large scale GPU systems. The proposed method uses a hybridization of the treecode and FMM, and combines the data-parallel treecode with the O(N) FMM. The run on TSUBAME 2.0 using 4096 GPUs achieved 74 % parallel efficiency, and the sustained performance reached 1.01 PFlops.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告計算機アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2011-11-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"29","bibliographicVolumeNumber":"2011-ARC-197"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"created":"2025-01-18T23:34:03.984748+00:00","id":79294,"links":{}}