{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:23:23Z","timestamp":1761110603877},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11704-021-0445-2","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T02:02:27Z","timestamp":1638496947000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["DRPS: efficient disk-resident parameter servers for distributed machine learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Zhen","family":"Song","sequence":"first","affiliation":[]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ge","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"445_CR1","doi-asserted-by":"crossref","unstructured":"Li M, Andersen D G, Park J W, Smola A J, Ahmed A, Josifovski V, Long J, Shekita E J, Su B Y. Scaling distributed machine learning with the parameter server. In: Proceedings of USENIX Symposium on Operating Systems Design and Implementation. 2014, 583\u2013598","DOI":"10.1145\/2640087.2644155"},{"key":"445_CR2","unstructured":"Chen T Q, Li M, Li Y T, Lin M, Wang N Y, Wang M J, Xiao T J, Xu B, Zhang C Y, Zhang Z. MXNet: a flexible and efficient machine learning library for heterogeneous distributed system. 2015, arXiv preprint arXiv: 1512.01274"},{"key":"445_CR3","doi-asserted-by":"crossref","unstructured":"Xing E P, Ho Q R, Dai W, Kim J K, Wei J L, Lee S H, Zheng X, Xie P T, Kumar A, Yu Y L. Petuum: a new platform for distributed machine learning on big data. In: Proceedings of ACM Conference on Knowledge Discovery and Data Mining. 2015, 1335\u20131344","DOI":"10.1145\/2783258.2783323"},{"key":"445_CR4","unstructured":"Abadi M, Barham P, Chen J M, Chen Z F, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray D G, Steiner B, Tucker P A, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X Q. TensorFlow: a system for large-scale machine learning. In: Proceedings of USENIX Symposium on Operating Systems Design and Implementation. 2016, 265\u2013283"},{"key":"445_CR5","unstructured":"Recht B, Re C, Wright S J, Niu F. Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Proceeding of the 24th International Conference on Neural Information Processing Systems. 2011, 693\u2013701"},{"issue":"4","key":"445_CR6","doi-asserted-by":"publisher","first-page":"446","DOI":"10.14778\/3297753.3297763","volume":"12","author":"D Xin","year":"2018","unstructured":"Xin D, Macke S, Ma L T, Liu J L, Song S C, Parameswaran A G. Helix: holistic optimization for accelerating iterative machine learning. Proceedings of the VLDB Endowment, 2018, 12(4): 446\u2013460","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"5","key":"445_CR7","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1145\/3187009.3177734","volume":"11","author":"Y Z Huang","year":"2018","unstructured":"Huang Y Z, Jin T, Wu Y D, Cai Z K, Yan X, Yang F, Li J F, Guo Y Y, Cheng J. FlexPS: flexible parallelism control in parameter server architecture. Proceedings of the VLDB Endowment, 2018, 11(5): 566\u2013579","journal-title":"Proceedings of the VLDB Endowment"},{"key":"445_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Z P, Cui B, Shao Y X, Yu L L, Jiang J W, Miao X P. PS2: parameter server on spark. In: Proceedings of ACM Conference on Management of Data. 2019, 376\u2013388","DOI":"10.1145\/3299869.3314038"},{"key":"445_CR9","unstructured":"Zaharia M, Chowdhury M, Franklin M J, Shenker S, Stoica I. Spark: cluster computing with working sets. In: Proceedings of USENIX Workshop on Hot Topics in Cloud Computing. 2010, 1\u20137"},{"key":"445_CR10","doi-asserted-by":"crossref","unstructured":"Cho M, Finkler U, Kung D S, Hunter H C. BlueConnect: decomposing all-reduce for deep learning on heterogeneous network hierarchy. In: Proceedings of Conference on Machine Learning and Systems. 2019, 1\u201311","DOI":"10.1147\/JRD.2019.2947013"},{"issue":"5","key":"445_CR11","doi-asserted-by":"publisher","first-page":"420","DOI":"10.14778\/2876473.2876477","volume":"9","author":"F Yang","year":"2016","unstructured":"Yang F, Li J F, Cheng J. Husky: towards a more efficient and expressive distributed computing framework. Proceedings of the VLDB Endowment, 2016, 9(5): 420\u2013431","journal-title":"Proceedings of the VLDB Endowment"},{"key":"445_CR12","unstructured":"Jiang Y M, Zhu Y B, Lan C, Yi B, Cui Y, Guo C X. A unified architecture for accelerating distributed dnn training in heterogeneous gpu\/cpu clusters. In: Proceedings of USENIX Symposium on Operating Systems Design and Implementation. 2020, 463\u2013479"},{"key":"445_CR13","doi-asserted-by":"crossref","unstructured":"Wang Z G, Gu Y, Bao Y B, Yu G, Yu J X. Hybrid pulling\/pushing for i\/o-efficient distributed and iterative graph computing. In: Proceedings of ACM Conference on Management of Data. 2016, 479\u2013494","DOI":"10.1145\/2882903.2882938"},{"issue":"10","key":"445_CR14","doi-asserted-by":"publisher","first-page":"986","DOI":"10.14778\/3115404.3115405","volume":"10","author":"C J Qin","year":"2017","unstructured":"Qin C J, Torres M, Rusu F. Scalable asynchronous gradient descent optimization for out-of-core models. Proceedings of the VLDB Endowment, 2017, 10(10): 986\u2013997","journal-title":"Proceedings of the VLDB Endowment"},{"key":"445_CR15","unstructured":"Li M, Andersen D G, Smola A J. Graph partitioning via parallel submodular approximation to accelerate distributed machine learning. 2015, arXiv preprint arXiv: 1505.04636"},{"issue":"12","key":"445_CR16","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.14778\/3407790.3407796","volume":"13","author":"A Renz-Wieland","year":"2020","unstructured":"Renz-Wieland A, Gemulla R, Zeuch S, Markl V. Dynamic parameter allocation in parameter servers. Proceedings of the VLDB Endowment, 2020, 13(12): 1877\u20131890","journal-title":"Proceedings of the VLDB Endowment"},{"key":"445_CR17","doi-asserted-by":"crossref","unstructured":"Chen Y R, Peng Y H, Bao Y X, Wu C, Zhu Y B, Guo C X. Elastic parameter server load distribution in deep learning clusters. In: Proceedings of ACM Symposium on Cloud Computing. 2020, 507\u2013521","DOI":"10.1145\/3419111.3421307"},{"issue":"1","key":"445_CR18","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s41019-020-00145-x","volume":"6","author":"B Gallet","year":"2021","unstructured":"Gallet B, Gowanlock M. Heterogeneous cpu-gpu epsilon grid joins: static and dynamic work partitioning strategies. Data Science and Engineering, 2021, 6(1): 39\u201362","journal-title":"Data Science and Engineering"},{"issue":"8","key":"445_CR19","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/79173.79181","volume":"33","author":"L G Valiant","year":"1990","unstructured":"Valiant L G. A bridging model for parallel computation. Communications of the ACM, 1990, 33(8): 103\u2013111","journal-title":"Communications of the ACM"},{"key":"445_CR20","unstructured":"Ho Q R, Cipar J, Cui H G, Lee S H, Kim J K, Gibbons P B, Gibson G A, Ganger G R, Xing E P. More effective distributed ML via a stale synchronous parallel parameter server. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 1223\u20131231"},{"key":"445_CR21","doi-asserted-by":"crossref","unstructured":"Li M, Andersen D G, Smola A J, Yu K. Communication efficient distributed machine learning with the parameter server. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 19\u201327","DOI":"10.1145\/2640087.2644155"},{"key":"445_CR22","doi-asserted-by":"crossref","unstructured":"Fan W F, Lu P, Luo X J, Xu J B, Yin Q, Yu W Y, Xu R Q. Adaptive asynchronous parallelization of graph algorithms. In: Proceedings of the International Conference on Management of Data. 2018, 1141\u20131156","DOI":"10.1145\/3183713.3196918"},{"key":"445_CR23","doi-asserted-by":"crossref","unstructured":"Jiang J W, Cui B, Zhang C, Yu L L. Heterogeneity-aware distributed parameter servers. In: Proceedings of the ACM International Conference on Management of Data. 2017, 463\u2013478","DOI":"10.1145\/3035918.3035933"},{"key":"445_CR24","doi-asserted-by":"crossref","unstructured":"Wang Z G, Gao L X, Gu Y, Bao Y B, Yu G. FSP: towards flexible synchronous parallel framework for expectation-maximization based algorithms on cloud. In: Proceedings of the Symposium on Cloud Computing. 2017, 1\u201314","DOI":"10.1145\/3127479.3128612"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-021-0445-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-021-0445-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-021-0445-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T21:04:08Z","timestamp":1695157448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-021-0445-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,3]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["445"],"URL":"https:\/\/doi.org\/10.1007\/s11704-021-0445-2","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,3]]},"assertion":[{"value":"6 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"164321"}}