{"created":"2025-01-19T00:44:20.857352+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00174113","sets":["934:1119:8503:8852"]},"path":["8852"],"owner":"11","recid":"174113","title":["共役勾配法への種々の通信削減手法の適用と評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-08-04"},"_buckets":{"deposit":"cbafba2a-503f-400d-bf20-db9d4407a7d1"},"_deposit":{"id":"174113","pid":{"type":"depid","value":"174113","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"共役勾配法への種々の通信削減手法の適用と評価","author_link":["356154","356151","356158","356155","356153","356156","356159","356160","356157","356152"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"共役勾配法への種々の通信削減手法の適用と評価"},{"subitem_title":"Application and Evaluation of Various Communication Avoiding Techniques for the Conjugate Gradient Method","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[並列数値アルゴリズム] 線形解法，通信削減アルゴリズム，共役勾配法，Matrix Powers Kernel","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2016-08-04","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"工学院大学"},{"subitem_text_value":"工学院大学"},{"subitem_text_value":"工学院大学"},{"subitem_text_value":"北海道大学"},{"subitem_text_value":"東京大学"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kogakuin University","subitem_text_language":"en"},{"subitem_text_value":"Kogakuin University","subitem_text_language":"en"},{"subitem_text_value":"Kogakuin University","subitem_text_language":"en"},{"subitem_text_value":"Hokkaido University","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","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/174113/files/IPSJ-TACS0903003.pdf","label":"IPSJ-TACS0903003.pdf"},"date":[{"dateType":"Available","dateValue":"2018-08-04"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TACS0903003.pdf","filesize":[{"value":"1.2 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":"11"},{"tax":["include_tax"],"price":"0","billingrole":"14"},{"tax":["include_tax"],"price":"0","billingrole":"15"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1c3dd930-dcf9-458b-be16-8a03a26857d2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"熊谷, 洋佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤井, 昭宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 輝雄"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"深谷, 猛"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"須田, 礼仁"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yosuke, Kumagai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akihiro, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Teruo, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takeshi, Fukaya","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Reiji, Suda","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11833852","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7829","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"スーパコンピュータの性能はコア数の増加とともに向上している．大規模な線形解法として共役勾配法（CG法）が広く用いられる．高並列な環境において，内積計算で発生する集団通信が深刻なボトルネックになると指摘されている．近年，Communication-avoiding CG法の一種としてChebyshev基底共役勾配法（CBCG法）が提案されている．本論文では，CBCG法で現れる集団通信の回数を減らしたCBCGR法を示し，CBCGR法に対して通信削減手法であるMatrix Powers Kernel（MPK）の適用を行った．また，2次元と3次元のPoisson方程式に対してFX10（oakleaf-fx）スーパコンピュータシステムで最大1,440ノードを使用したOpenMP/MPIのHybrid並列での計測を行った．2次元Poisson方程式ではCBCGR法およびCBCGR-MPK法が一定の並列数以上でCG法およびCBCG法よりも高速になり，3次元Poisson方程式では一定の並列数以上でCBCGR法が高速となった．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The performance of supercomputers improves as the number of cores increases. The conjugate gradient (CG) method is useful for solving large and sparse linear systems. It has been pointed out that collective communication needed for calculating inner products becomes serious bottleneck when executing the CG method on massively parallel systems. Recently, the Chebyshev basis CG (CBCG) method, a variant of the Communication-avoiding CG method, has been proposed. In this paper, we reduced collective communication of CBCG method (CBCGR) and applied Matrix Powers Kernel (MPK) for CBCGR method. We then measured the execution time of these methods for 2D and 3D Poisson problems using OpenMP/MPI hybrid parallel model on the FX10 (oakleaf-fx) supercomputer system. For the 2D-Poisson problem, the CBCGR and CBCGR-MPK methods are faster than the CG and CBCG methods when the number of processes is sufficiently large. For the 3D-Poisson problem, the CBCGR method is faster than the CG and CBCG methods when the number of processes is sufficeint large.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"13","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌コンピューティングシステム（ACS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2016-08-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"9"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":174113,"updated":"2025-01-20T07:01:58.768838+00:00","links":{}}