{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213134","sets":["1164:2240:10556:10713"]},"path":["10713"],"owner":"44499","recid":"213134","title":["大規模グラフニューラルネットワークにおける実行性能とモデル精度のトレードオフに関する評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-09-20"},"_buckets":{"deposit":"29752115-0f72-4aa9-9ae9-20c7c9d86f60"},"_deposit":{"id":"213134","pid":{"type":"depid","value":"213134","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"大規模グラフニューラルネットワークにおける実行性能とモデル精度のトレードオフに関する評価","author_link":["544784","544785","544787","544788","544786"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模グラフニューラルネットワークにおける実行性能とモデル精度のトレードオフに関する評価"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"高性能計算","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-09-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"産業技術総合研究所情報・人間工学領域"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"産業技術総合研究所情報・人間工学領域"},{"subitem_text_value":"産業技術総合研究所情報・人間工学領域"},{"subitem_text_value":"産業技術総合研究所情報・人間工学領域"}]},"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/213134/files/IPSJ-HPC21181001.pdf","label":"IPSJ-HPC21181001.pdf"},"date":[{"dateType":"Available","dateValue":"2023-09-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HPC21181001.pdf","filesize":[{"value":"1.5 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":"3ef10c45-28b7-4276-861a-63bf324b6c10","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"金刺, 宏樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴村, 豊太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"劉, 欣"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"広渕, 崇宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高野, 了成"}],"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":"グラフデータの大規模化とグラフ構造を用いた機械学習アプリケーションが多様化するにつれて，グラフニューラルネットワーク（GNN）は他の深層学習モデルと同様，GPU や大容量 DRAM などより高性能な計算環境が必要とされている．しかし，グラフデータサイズと比較して GNN の各層のサイズ，学習率などが適切でない場合，GPU デバイスメモリ容量が不足するか，モデル精度が限界になるなどの問題が起こる．本研究では，代表的な大規模グラフデータ，GNN モデルおよび学習用ハイパーパラメータについて，学習時間モデル精度の推移を比較することにより，実行性能とモデル精度のトレードオフを明らかにしながら最適な実行条件を探索する．評価実験により，GNN モデルとそれぞれのハイパーパラメータの違いにより学習開始時から収束するまでの数秒間で顕著な差が見られた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ハイパフォーマンスコンピューティング（HPC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-09-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021-HPC-181"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213134,"updated":"2025-01-19T17:16:00.608982+00:00","links":{},"created":"2025-01-19T01:14:02.743714+00:00"}