{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02002273","sets":["1164:1579:11896:1748234218909"]},"path":["1748234218909"],"owner":"80578","recid":"2002273","title":["アウトオブオーダCPU向け分岐予測アルゴリズムRUNLTSの提案とChampionship Branch Prediction 2025フレームワークを用いた評価"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-06-02"},"_buckets":{"deposit":"a82596df-93a2-4de0-af2a-1bfa2d0e7352"},"_deposit":{"id":"2002273","pid":{"type":"depid","value":"2002273","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"アウトオブオーダCPU向け分岐予測アルゴリズムRUNLTSの提案とChampionship Branch Prediction 2025フレームワークを用いた評価","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"アウトオブオーダCPU向け分岐予測アルゴリズムRUNLTSの提案とChampionship Branch Prediction 2025フレームワークを用いた評価","subitem_title_language":"ja"},{"subitem_title":"RUNLTS: A Branch Prediction Algorithm for Out-of-Order CPUs and Its Evaluation Using the Championship Branch Prediction 2025 Framework","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"アーキテクチャ","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2025-06-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学大学院工学研究科"},{"subitem_text_value":"名古屋工業大学大学院工学研究科"},{"subitem_text_value":"名古屋工業大学大学院工学研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"名古屋工業大学大学院工学研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Nagoya Institute of Technology","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 Engineering, Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, 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/2002273/files/IPSJ-ARC25261037.pdf","label":"IPSJ-ARC25261037.pdf"},"date":[{"dateType":"Available","dateValue":"2999-12-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC25261037.pdf","filesize":[{"value":"2.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"644a6bfd-eeba-4ede-a174-11b7c6cea221","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"水野,将成"}]},{"creatorNames":[{"creatorName":"小泉,透"}]},{"creatorNames":[{"creatorName":"前川,隼輝"}]},{"creatorNames":[{"creatorName":"黒木,地球"}]},{"creatorNames":[{"creatorName":"津邑,公暁"}]},{"creatorNames":[{"creatorName":"塩谷,亮太"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masanari Mizuno","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Toru Koizumi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Toshiki Maekawa","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Maru Kuroki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tomoaki Tsumura","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Ryota Shioya","creatorNameLang":"en"}]}]},"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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8574","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年のCPUは高性能化の要求に応えるために投機実行の規模が拡大しており，分岐予測ミスが性能に及ぼす影響はかつてなく大きくなっている．こうした状況を踏まえ9年ぶりに開催される「Championship Branch Prediction 2025（CBP2025）」では，従来と異なりプロセッサパイプラインの情報が提供され，それをうまく活用できるかに注目が集まっている．我々がCBP2025に提出した分岐予測器RUNLTSは，TAGE-SCを基盤としつつ，より良い履歴長選択方法の提案及びレジスタ値と分岐方向の相関をとらえる新機構の導入などにより予測精度向上を図った．本稿では，TAGE-SC-Lをはじめとする既存の分岐予測器と我々のRUNLTSを比較し，CBP2025で提供されたフレームワークを用いた評価結果を報告する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"To meet growing performance demands, modern CPUs increasingly rely on large-scale speculative execution, making the cost of branch mispredictions more critical than ever. In light of this situation, the Championship Branch Prediction 2025 (CBP2025) competition will be held for the first time in nine years and provides a full out-of-order pipeline simulation environment. This enables participants to leverage pipeline-level information that was previously unavailable. In this paper, we introduce RUNLTS, a novel branch predictor based on TAGE-SC. RUNLTS employs an improved history length selection scheme and a new mechanism to capture correlations between register values and branch outcomes. We evaluate RUNLTS against existing predictors, including TAGE-SC-L, using the CBP2025 framework, and demonstrate superior prediction accuracy.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システム・アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2025-06-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"37","bibliographicVolumeNumber":"2025-ARC-261"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"updated":"2025-05-26T04:58:16.190902+00:00","created":"2025-05-26T04:58:12.199471+00:00","id":2002273}