{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:59:15Z","timestamp":1757624355002,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3731545.3736815","type":"proceedings-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T12:46:16Z","timestamp":1757421976000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Factors Impacting I\/O Time Proportion in AI Workloads"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1484-229X","authenticated-orcid":false,"given":"Zoya","family":"Masih","sequence":"first","affiliation":[{"name":"GWDG\/ University of Goettingen, G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2506-1841","authenticated-orcid":false,"given":"Radita","family":"Liem","sequence":"additional","affiliation":[{"name":"RWTH Aachen University, Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6915-1179","authenticated-orcid":false,"given":"Julian","family":"Kunkel","sequence":"additional","affiliation":[{"name":"GWDG \/ University of Goettingen, G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3611007"},{"key":"e_1_3_2_1_2_1","volume-title":"Classification and regression trees","author":"Breiman Leo","unstructured":"Leo Breiman, Jerome Friedman, Richard A Olshen, and Charles J Stone. 2017. Classification and regression trees. Routledge."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the Cray User Group meeting.","volume":"2013","author":"Carns Philip","year":"2013","unstructured":"Philip Carns, Yushu Yao, Kevin Harms, Robert Latham, Robert Ross, and Katie Antypas. 2013. Production i\/o characterization on the cray xe6. In Proceedings of the Cray User Group meeting. Vol. 2013."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"H. Devarajan H. Zheng A. Kougkas X.-H. Sun and V. Vishwanath. 2021. DLIO: a data-centric benchmark for scientific deep learning applications. 81\u201391.","DOI":"10.1109\/CCGrid51090.2021.00018"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6767"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Andreas Kn\u00fcpfer et al. 2012. Score-p: a joint performance measurement runtime infrastructure for periscope scalasca tau and vampir. In Tools for High Performance Computing 2011. Holger Brunst Matthias S. M\u00fcller Wolfgang E. Nagel and Michael M. Resch (Eds.) Springer Berlin Heidelberg Berlin Heidelberg 79\u201391. ISBN: 978-3-642-31476-6.","DOI":"10.1007\/978-3-642-31476-6_7"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-20119-1_19"},{"key":"e_1_3_2_1_11_1","volume-title":"Advances in Neural Information Processing Systems.","author":"Lundberg Scott M","year":"2022","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems. Vol. 30. Curran Associates, Inc. Retrieved Sept. 19, 2022 from https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html."},{"key":"e_1_3_2_1_12_1","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"F. Pedregosa","year":"2011","unstructured":"F. Pedregosa et al. 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/PADSW.2018.8644613"}],"event":{"name":"HPDC '25: 34th International Symposium on High-Performance Parallel and Distributed Computing","location":"University of Notre Dame Conference Facilities Notre Dame IN USA","acronym":"HPDC '25","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3731545.3736815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T12:46:24Z","timestamp":1757421984000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3731545.3736815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":13,"alternative-id":["10.1145\/3731545.3736815","10.1145\/3731545"],"URL":"https:\/\/doi.org\/10.1145\/3731545.3736815","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-09-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}