{"created":"2025-01-19T01:19:17.024916+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218941","sets":["1164:2240:10902:10971"]},"path":["10971"],"owner":"44499","recid":"218941","title":["空間充填曲線を用いたベクトルプロセッサにおけるk近傍法の高速化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-20"},"_buckets":{"deposit":"e4db2215-1b65-4266-9777-22649076e535"},"_deposit":{"id":"218941","pid":{"type":"depid","value":"218941","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"空間充填曲線を用いたベクトルプロセッサにおけるk近傍法の高速化","author_link":["570413","570412","570411"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"空間充填曲線を用いたベクトルプロセッサにおけるk近傍法の高速化"},{"subitem_title":"Speeding up k-nearest neighbors search with space-filling curve using vector processors","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"SIMD・ベクトル","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-07-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本電気株式会社(デジタルテクノロジー開発研究所)"},{"subitem_text_value":"日本電気株式会社(デジタルテクノロジー開発研究所)"},{"subitem_text_value":"日本電気株式会社(デジタルテクノロジー開発研究所)"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"NEC Corporation(Digital Technology Development Laboratory)","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation(Digital Technology Development Laboratory)","subitem_text_language":"en"},{"subitem_text_value":"NEC Corporation(Digital Technology Development Laboratory)","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/218941/files/IPSJ-HPC22185003.pdf","label":"IPSJ-HPC22185003.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HPC22185003.pdf","filesize":[{"value":"2.7 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":"14"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c1bfdf04-b3cd-4df9-b0fb-8a29647beef9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小寺, 雅司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"サハ, ソウラブ"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒木, 拓也"}],"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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8841","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，空間充填曲線を用いることでベクトルプロセッサに適した形で k 近傍法を高速化する手法を提案し，SX-Aurora TSUBASA を用いて k 近傍を高速化した結果について報告する．k 近傍法はトレーニングデータとクエリデータの距離計算が計算の大部分を占めている．提案手法では空間充填曲線を用いることでトレーニングデータとクエリデータの距離計算を減らし，ベクトルプロセッサを用いることで計算を高速化する．性能評価の結果，scikit-learn を用いた k 近傍法と比べ，SX-Aurora TSUBASA を用いた k 近傍法計算は最大 7 倍程度高速に計算できることを確認した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a method to perform k-nearest neighbors search with a space-filling curve method using a processing scheme suitable for vector processors, and report the results of speeding up k-nearest neighbors search using SX-Aurora TSUBASA. In the k-nearest neighbors search, the distance between training data and query data accounts for a large part of the computation. The proposed method speeds up the process by reducing the distance computation between training data and query data by using a space-filling curve. The performance evaluation results show that the k-nearest neighbors search using SX-Aurora TSUBASA is up to seven times faster than the k nearest neighbor method using scikit-learn.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"9","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":"2022-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2022-HPC-185"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218941,"updated":"2025-01-19T14:58:54.627928+00:00","links":{}}