{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:54:07Z","timestamp":1778309647150,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The National Natural Foundation of China","award":["NO. U1811461."],"award-info":[{"award-number":["NO. U1811461."]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,7]]},"DOI":"10.1145\/3605573.3605610","type":"proceedings-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T16:21:16Z","timestamp":1694622076000},"page":"82-91","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PSRA-HGADMM: A Communication Efficient Distributed ADMM Algorithm"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-1034","authenticated-orcid":false,"given":"Yongwen","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8010-5545","authenticated-orcid":false,"given":"Yongmei","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5260-3458","authenticated-orcid":false,"given":"Guozheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International conference on machine learning. PMLR, 173\u2013182","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, 2016. Deep speech 2: End-to-end speech recognition in english and mandarin. In International conference on machine learning. PMLR, 173\u2013182."},{"key":"e_1_3_2_1_2_1","volume-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends\u00ae in Machine learning 3, 1","author":"Boyd Stephen","year":"2011","unstructured":"Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends\u00ae in Machine learning 3, 1 (2011), 1\u2013122."},{"key":"e_1_3_2_1_3_1","first-page":"1","article-title":"GADMM: Fast and communication efficient framework for distributed machine learning.J","volume":"21","author":"Elgabli Anis","year":"2020","unstructured":"Anis Elgabli, Jihong Park, Amrit\u00a0S Bedi, Mehdi Bennis, and Vaneet Aggarwal. 2020. GADMM: Fast and communication efficient framework for distributed machine learning.J. Mach. Learn. Res. 21, 76 (2020), 1\u201339.","journal-title":"Mach. Learn. Res."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3026398"},{"key":"e_1_3_2_1_6_1","volume-title":"Using MPI: portable parallel programming with the message-passing interface. Vol.\u00a01","author":"Gropp William","unstructured":"William Gropp, William\u00a0D Gropp, Ewing Lusk, Anthony Skjellum, and Argonne Distinguished Fellow Emeritus\u00a0Ewing Lusk. 1999. Using MPI: portable parallel programming with the message-passing interface. Vol.\u00a01. MIT press."},{"key":"e_1_3_2_1_7_1","volume-title":"Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409","author":"Hestness Joel","year":"2017","unstructured":"Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou. 2017. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409 (2017)."},{"key":"e_1_3_2_1_8_1","first-page":"1223","article-title":"More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server","volume":"2013","author":"Ho Qirong","year":"2013","unstructured":"Qirong Ho, James Cipar, Henggang Cui, Jin\u00a0Kyu Kim, and Eric\u00a0P Xing. 2013. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. Advances in Neural Information Processing Systems 2013, 2013 (2013), 1223.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom)","author":"Huang Xin","unstructured":"Xin Huang, Guozheng Wang, and Yongmei Lei. 2021. GR-ADMM: A Communication Efficient Algorithm Based on ADMM. In 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom). IEEE, 220\u2013227."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00065"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3054775"},{"key":"e_1_3_2_1_12_1","volume-title":"Communication efficient distributed machine learning with the parameter server. Advances in Neural Information Processing Systems 27","author":"Li Mu","year":"2014","unstructured":"Mu Li, David\u00a0G Andersen, Alexander\u00a0J Smola, and Kai Yu. 2014. Communication efficient distributed machine learning with the parameter server. Advances in Neural Information Processing Systems 27 (2014)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2957719"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273567"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304009"},{"key":"e_1_3_2_1_16_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou","author":"Silver David","year":"2016","unstructured":"David Silver, Aja Huang, Chris\u00a0J Maddison, Arthur Guez, Laurent Sifre, George Van Den\u00a0Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, 2016. Mastering the game of Go with deep neural networks and tree search. nature 529, 7587 (2016), 484\u2013489."},{"key":"e_1_3_2_1_17_1","volume-title":"Advances in neural information processing systems","author":"Sohn K","year":"2015","unstructured":"K Sohn, H Lee, X Yan, C Cortes, N Lawrence, and D Lee. 2015. Advances in neural information processing systems. Neural Information Processing Systems Foundation, Curran Associates, Inc (2015), 3483\u20133491."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3027032"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/79173.79181"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03590-7"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30709-7_27"},{"key":"e_1_3_2_1_23_1","unstructured":"Zheng Xu Mario Figueiredo and Tom Goldstein. 2017. Adaptive ADMM with spectral penalty parameter selection. In Artificial Intelligence and Statistics. PMLR 718\u2013727."},{"key":"e_1_3_2_1_24_1","volume-title":"Andrew\u00a0L Beam, and Isaac\u00a0S Kohane","author":"Yu Kun-Hsing","year":"2018","unstructured":"Kun-Hsing Yu, Andrew\u00a0L Beam, and Isaac\u00a0S Kohane. 2018. Artificial intelligence in healthcare. Nature biomedical engineering 2, 10 (2018), 719\u2013731."},{"key":"e_1_3_2_1_25_1","volume-title":"2017 USENIX Annual Technical Conference (USENIX ATC 17)","author":"Zhang Hao","year":"2017","unstructured":"Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, and Eric\u00a0P Xing. 2017. Poseidon: An efficient communication architecture for distributed deep learning on { GPU} clusters. In 2017 USENIX Annual Technical Conference (USENIX ATC 17). 181\u2013193."},{"key":"e_1_3_2_1_26_1","volume-title":"International conference on machine learning. PMLR, 1701\u20131709","author":"Zhang Ruiliang","year":"2014","unstructured":"Ruiliang Zhang and James Kwok. 2014. Asynchronous distributed ADMM for consensus optimization. In International conference on machine learning. PMLR, 1701\u20131709."}],"event":{"name":"ICPP 2023: 52nd International Conference on Parallel Processing","location":"Salt Lake City UT USA","acronym":"ICPP 2023"},"container-title":["Proceedings of the 52nd International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3605573.3605610","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3605573.3605610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:04Z","timestamp":1750182544000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3605573.3605610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":25,"alternative-id":["10.1145\/3605573.3605610","10.1145\/3605573"],"URL":"https:\/\/doi.org\/10.1145\/3605573.3605610","relation":{},"subject":[],"published":{"date-parts":[[2023,8,7]]},"assertion":[{"value":"2023-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}