{"id":218139,"updated":"2025-01-19T15:15:23.623704+00:00","links":{},"created":"2025-01-19T01:18:33.502687+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218139","sets":["934:935:10774:10914"]},"path":["10914"],"owner":"44499","recid":"218139","title":["Future Possibilities and Effectiveness of JIT from Elixir Code of Image Processing and Machine Learning into Native Code with SIMD Instructions"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-20"},"_buckets":{"deposit":"74ecfcf6-0f79-4f93-9370-447ade5008e7"},"_deposit":{"id":"218139","pid":{"type":"depid","value":"218139","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Future Possibilities and Effectiveness of JIT from Elixir Code of Image Processing and Machine Learning into Native Code with SIMD Instructions","author_link":["566657","566658"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Future Possibilities and Effectiveness of JIT from Elixir Code of Image Processing and Machine Learning into Native Code with SIMD Instructions"},{"subitem_title":"Future Possibilities and Effectiveness of JIT from Elixir Code of Image Processing and Machine Learning into Native Code with SIMD Instructions","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[発表概要, Unrefereed Presentatin Abstract] ","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2022-05-20","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Faculty of Environmental Engineering, The University of Kitakyushu"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Environmental Engineering, The University of Kitakyushu","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/218139/files/IPSJ-TPRO1502011.pdf","label":"IPSJ-TPRO1502011.pdf"},"date":[{"dateType":"Available","dateValue":"2024-05-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TPRO1502011.pdf","filesize":[{"value":"29.5 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"15"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"40600cd3-4a41-447a-9e7d-24f3f88e6683","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Susumu, Yamazaki"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Susumu, Yamazaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464814","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":"1882-7802","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Nx is a multi-dimensional tensor library for Elixir with multi-staged compilation to the CPU or GPU, which is similar to NumPy and TensorFlow in Python. Nx is expected to be applied in image processing and machine learning. Code used by them in C is often optimized for CPUs into native code with SIMD instructions. In this presentation, we'll show that native code with SIMD instructions is 1000x+ faster than equivalent Elixir code with Nx, to evaluate future possibilities and effectiveness of such code generation and optimization. One of our future works is to implement code generation into BeamAsm, which is a JIT for Erlang VM, which is the backend of Elixir, though it doesn't generate SIMD instructions, now.","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Nx is a multi-dimensional tensor library for Elixir with multi-staged compilation to the CPU or GPU, which is similar to NumPy and TensorFlow in Python. Nx is expected to be applied in image processing and machine learning. Code used by them in C is often optimized for CPUs into native code with SIMD instructions. In this presentation, we'll show that native code with SIMD instructions is 1000x+ faster than equivalent Elixir code with Nx, to evaluate future possibilities and effectiveness of such code generation and optimization. One of our future works is to implement code generation into BeamAsm, which is a JIT for Erlang VM, which is the backend of Elixir, though it doesn't generate SIMD instructions, now.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌プログラミング（PRO）"}],"bibliographicPageStart":"7","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"15"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}