{"created":"2025-01-19T00:44:25.615677+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00174201","sets":["934:989:8398:8850"]},"path":["8850"],"owner":"11","recid":"174201","title":["Performance Evaluation of Golub-Kahan-Lanczos Algorithm with Reorthogonalization by Classical Gram-Schmidt Algorithm and OpenMP"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-08-10"},"_buckets":{"deposit":"3746624a-ddaa-4515-aa97-3d3c6da42932"},"_deposit":{"id":"174201","pid":{"type":"depid","value":"174201","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Performance Evaluation of Golub-Kahan-Lanczos Algorithm with Reorthogonalization by Classical Gram-Schmidt Algorithm and OpenMP","author_link":["356493","356487","356490","356495","356485","356488","356492","356494","356486","356489","356491","356484"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Performance Evaluation of Golub-Kahan-Lanczos Algorithm with Reorthogonalization by Classical Gram-Schmidt Algorithm and OpenMP"},{"subitem_title":"Performance Evaluation of Golub-Kahan-Lanczos Algorithm with Reorthogonalization by Classical Gram-Schmidt Algorithm and OpenMP","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] subset computation of singular pairs, Golub-Kahan-Lanczos algorithm with reorthogonalization, classical Gram-Schmidt algorithm with reorthogonalization, OpenMP, sharedmemory multi-core processing","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2016-08-10","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Research Group of Information and Communication Technology for Life, Nara Women's University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Research Group of Information and Communication Technology for Life, Nara Women's University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","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/174201/files/IPSJ-TOM0902002.pdf","label":"IPSJ-TOM0902002.pdf"},"date":[{"dateType":"Available","dateValue":"2018-08-10"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM0902002.pdf","filesize":[{"value":"535.2 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c3fc616a-c765-43c9-b8a0-2851acef71e8","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":"Masami, Takata"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Ishigami"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kinji, Kimura"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuki, Fujii"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroki, Tanaka"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshimasa, Nakamura"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masami, Takata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Ishigami","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kinji, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuki, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroki, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshimasa, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","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":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"The Golub-Kahan-Lanczos algorithm with reorthogonalization (GKLR algorithm) is an algorithm for computing a subset of singular triplets of large-scale sparse matrices. The reorthogonalization tends to become a bottleneck of the execution time, as the iteration number of the GKLR algorithm increases. In this paper, OpenMP-based parallel implementation of the classical Gram-Schmidt algorithm with reorthogonalization (OMP-CGS2 algorithm) is introduced. The OMP-CGS2 algorithm has the advantage of data reusability and is expected to achieve higher performance of the reorthogonalization computations on shared-memory multi-core processors with large caches than the conventional reorthogonalization algorithms. Numerical experiments on shared-memory multi-core processors show that the OMP-CGS2 algorithm accelerates the GKLR algorithm more effectively for computing a subset of singular triplets of some matrices than the conventional reorthogonalization algorithms. In addition, we discuss the cache utilization in the OMP-CGS2 algorithm and a condition that the OMP-CGS2 algorithm achieves higher performance than the CGS2 algorithm.","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The Golub-Kahan-Lanczos algorithm with reorthogonalization (GKLR algorithm) is an algorithm for computing a subset of singular triplets of large-scale sparse matrices. The reorthogonalization tends to become a bottleneck of the execution time, as the iteration number of the GKLR algorithm increases. In this paper, OpenMP-based parallel implementation of the classical Gram-Schmidt algorithm with reorthogonalization (OMP-CGS2 algorithm) is introduced. The OMP-CGS2 algorithm has the advantage of data reusability and is expected to achieve higher performance of the reorthogonalization computations on shared-memory multi-core processors with large caches than the conventional reorthogonalization algorithms. Numerical experiments on shared-memory multi-core processors show that the OMP-CGS2 algorithm accelerates the GKLR algorithm more effectively for computing a subset of singular triplets of some matrices than the conventional reorthogonalization algorithms. In addition, we discuss the cache utilization in the OMP-CGS2 algorithm and a condition that the OMP-CGS2 algorithm achieves higher performance than the CGS2 algorithm.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2016-08-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"9"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":174201,"updated":"2025-01-20T06:59:30.957567+00:00","links":{}}