{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:24:11Z","timestamp":1740108251058,"version":"3.37.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Basic Algorithms and Programming Environment of Big Data Analysis Based on Supercomputing","award":["U1811461"],"award-info":[{"award-number":["U1811461"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00607-021-00968-0","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T21:02:31Z","timestamp":1623963751000},"page":"2737-2762","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hybrid MPI\/OpenMP parallel asynchronous distributed alternating direction method of multipliers"],"prefix":"10.1007","volume":"103","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4662-6573","authenticated-orcid":false,"given":"Dongxia","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongmei","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"968_CR1","doi-asserted-by":"publisher","unstructured":"Balamurugan P, Posinasetty A, Shevade S (2016) ADMM for training sparse structural SVMs with augmented L1 regularizers. In: SIAM international conference on data mining. https:\/\/doi.org\/10.1137\/1.9781611974348.77","DOI":"10.1137\/1.9781611974348.77"},{"issue":"1","key":"968_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein J et al (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends\u00ae Mach Learn 3(1):1\u2013122. https:\/\/doi.org\/10.1561\/2200000016","journal-title":"Found Trends\u00ae Mach Learn"},{"issue":"12","key":"968_CR3","doi-asserted-by":"publisher","first-page":"3118","DOI":"10.1109\/TSP.2016.2537271","volume":"64","author":"TH Chang","year":"2016","unstructured":"Chang TH, Hong M, Liao WC, Wang X (2016) Asynchronous distributed ADMM for large-scale optimization-part i: algorithm and convergence analysis. IEEE Trans Signal Process 64(12):3118\u20133130. https:\/\/doi.org\/10.1109\/TSP.2016.2537271","journal-title":"IEEE Trans Signal Process"},{"issue":"12","key":"968_CR4","doi-asserted-by":"publisher","first-page":"3131","DOI":"10.1109\/TSP.2016.2537261","volume":"64","author":"TH Chang","year":"2016","unstructured":"Chang TH, Liao WC, Hong M, Wang X (2016) Asynchronous distributed ADMM for large-scale optimization-part ii: linear convergence analysis and numerical performance. IEEE Trans Signal Process 64(12):3131\u20133144. https:\/\/doi.org\/10.1109\/TSP.2016.2537261","journal-title":"IEEE Trans Signal Process"},{"issue":"3","key":"968_CR5","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.jocs.2010.05.001","volume":"1","author":"MJ Chorley","year":"2010","unstructured":"Chorley MJ, Walker DW (2010) Performance analysis of a hybrid MPI\/OpenMP application on multi-core clusters. J Comput Sci 1(3):168\u2013174. https:\/\/doi.org\/10.1016\/j.jocs.2010.05.001","journal-title":"J Comput Sci"},{"key":"968_CR6","doi-asserted-by":"crossref","unstructured":"Dhar S, Yi C, Ramakrishnan N, Shah M (2015) ADMM based scalable machine learning on spark. In: 2015 IEEE international conference on big data (Big Data), IEEE","DOI":"10.1109\/BigData.2015.7363871"},{"key":"968_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/e21121184","author":"S Feng","year":"2019","unstructured":"Feng S, Hua X, Wang Y, Lan Q, Zhu X (2019) Matrix information geometry for signal detection via hybrid MPI\/OpenMP. Entropy. https:\/\/doi.org\/10.3390\/e21121184","journal-title":"Entropy"},{"key":"968_CR8","unstructured":"Hsia CY, Zhu Y, Lin CJ (2017) A study on trust region update rules in newton methods for large-scale linear classification. In: Proceedings of machine learning research, PMLR, vol\u00a077, pp 33\u201348"},{"key":"968_CR9","doi-asserted-by":"crossref","unstructured":"Ibrahim DS, Hamdy S (2017) Hybrid MPI\/OpenMP implementation of PCA. In: 2017 eighth international conference on intelligent computing and information systems (ICICIS), IEEE","DOI":"10.1109\/INTELCIS.2017.8260048"},{"issue":"9","key":"968_CR10","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.parco.2011.02.002","volume":"37","author":"H Jin","year":"2011","unstructured":"Jin H, Jespersen D, Mehrotra P, Biswas R, Huang L, Chapman B (2011) High performance computing using MPI and OpenMP on multi-core parallel systems. Parallel Comput 37(9):562\u2013575. https:\/\/doi.org\/10.1016\/j.parco.2011.02.002","journal-title":"Parallel Comput"},{"key":"968_CR11","doi-asserted-by":"publisher","first-page":"42280","DOI":"10.1109\/ACCESS.2019.2907885","volume":"7","author":"W Kwedlo","year":"2019","unstructured":"Kwedlo W, Czochanski PJ (2019) A hybrid MPI\/OpenMP parallelization of k-means algorithms accelerated using the triangle inequality. IEEE Access 7:42280\u201342297","journal-title":"IEEE Access"},{"issue":"2","key":"968_CR12","doi-asserted-by":"publisher","first-page":"493","DOI":"10.32604\/cmc.2019.05178","volume":"58","author":"Y Li","year":"2019","unstructured":"Li Y, Wang X, Fang W, Xue F, Li X (2019) A distributed ADMM approach for collaborative regression learning in edge computing. Comput Mater Contin 58(2):493\u2013508","journal-title":"Comput Mater Contin"},{"key":"968_CR13","doi-asserted-by":"crossref","unstructured":"Lubell-Doughtie P, Sondag J (2013) Practical distributed classification using the alternating direction method of multipliers algorithm. In: IEEE international conference on big data, IEEE, vol\u00a01, pp 773\u2013776","DOI":"10.1109\/BigData.2013.6691651"},{"issue":"2","key":"968_CR14","first-page":"83","volume":"9","author":"L Smith","year":"2001","unstructured":"Smith L, Bull M (2001) Development of mixed mode MPI\/OpenMP. Sci Program 9(2):83\u201398","journal-title":"Sci Program"},{"key":"968_CR15","doi-asserted-by":"crossref","unstructured":"Song C, Yoon S, Pavlovic V (2016) Fast ADMM algorithm for distributed optimization with adaptive penalty. In: 30th AAAI conference on artificial intelligence, pp 753\u2013759","DOI":"10.1609\/aaai.v30i1.10069"},{"key":"968_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03590-7","author":"D Wang","year":"2021","unstructured":"Wang D, Lei Y, Xie J, Wang G (2021) HSAC-ALADMM: an asynchronous lazy ADMM algorithm based on hierarchical sparse allreduce communication. J Supercomput. https:\/\/doi.org\/10.1007\/s11227-020-03590-7","journal-title":"J Supercomput"},{"key":"968_CR17","doi-asserted-by":"crossref","unstructured":"Wang H, Gao Y, Shi Y, Wang H (2016) A fast distributed classification algorithm for large-scale imbalanced data. In: 16th IEEE international conference on data mining (ICDM), IEEE, pp 1251\u20131256","DOI":"10.1109\/ICDM.2016.0168"},{"issue":"11","key":"968_CR18","doi-asserted-by":"publisher","first-page":"3568","DOI":"10.1109\/TCYB.2016.2570808","volume":"47","author":"H Wang","year":"2017","unstructured":"Wang H, Gao Y, Shi Y, Wang R (2017) Group-based alternating direction method of multipliers for distributed linear classification. IEEE Trans Cybern 47(11):3568\u20133582","journal-title":"IEEE Trans Cybern"},{"key":"968_CR19","doi-asserted-by":"crossref","unstructured":"Wang S, Lei Y (2018) Fast communication structure for asynchronous distributed ADMM under unbalance process arrival pattern. In: 27th international conference on artificial neural networks, Springer","DOI":"10.1007\/978-3-030-01418-6_36"},{"key":"968_CR20","first-page":"1937","volume":"10","author":"K Woodsend","year":"2009","unstructured":"Woodsend K, Gondzio J (2009) Hybrid MPI\/OpenMP parallel linear support vector machine training. J Mach Learn Res 10:1937\u20131953","journal-title":"J Mach Learn Res"},{"key":"968_CR21","doi-asserted-by":"crossref","unstructured":"Xie J, Lei Y (2019) ADMMLIB: A library of communication-efficient AD-ADMM for distributed machine learning. In: FIP international conference on network and parallel computing","DOI":"10.1007\/978-3-030-30709-7_27"},{"key":"968_CR22","unstructured":"Xu Z, Taylor G, Li H, Figueiredo M, Goldstein T (2017) Adaptive consensus ADMM for distributed optimization"},{"issue":"2","key":"968_CR23","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s40571-018-0213-8","volume":"6","author":"B Yan","year":"2018","unstructured":"Yan B, Regueiro RA (2018) Comparison between pure MPI and hybrid MPI-OpenMP parallelism for discrete element method (DEM) of ellipsoidal and poly-ellipsoidal particles. Comput Particle Mech 6(2):271\u2013295","journal-title":"Comput Particle Mech"},{"issue":"2","key":"968_CR24","first-page":"3183","volume":"11","author":"GX Yuan","year":"2010","unstructured":"Yuan GX, Chang KW, Hsieh CJ, Lin CJ (2010) A comparison of optimization methods and software for large-scale L1-regularized linear classification. J Mach Learn Res 11(2):3183\u20133234","journal-title":"J Mach Learn Res"},{"key":"968_CR25","unstructured":"Zhang R, Kwok J (2014) Asynchronous distributed ADMM for consensus optimization. In: Proceedings of the 31th international conference on machine learning, JMLR, pp. 1701\u20131709"},{"key":"968_CR26","first-page":"289","volume":"2227","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Xiao L (2015) Communication-efficient distributed optimization of self-concordant empirical loss. Mathematics 2227:289\u2013341","journal-title":"Mathematics"},{"key":"968_CR27","doi-asserted-by":"crossref","unstructured":"Zhuang Y, Chin WS, Juan YC, Lin CJ (2015) Distributed newton methods for regularized logistic regression. In: 19th Pacific-Asia conference on knowledge discovery and data mining (PAKDD), vol 9078, pp 690\u2013703","DOI":"10.1007\/978-3-319-18032-8_54"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00968-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-021-00968-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00968-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T14:51:09Z","timestamp":1672498269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-021-00968-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":27,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["968"],"URL":"https:\/\/doi.org\/10.1007\/s00607-021-00968-0","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"type":"print","value":"0010-485X"},{"type":"electronic","value":"1436-5057"}],"subject":[],"published":{"date-parts":[[2021,6,17]]},"assertion":[{"value":"18 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}