{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:45:59Z","timestamp":1742996759843,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030602581"},{"type":"electronic","value":"9783030602598"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60259-8_50","type":"book-chapter","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T10:04:33Z","timestamp":1602756273000},"page":"685-699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hylo: Hybrid Layer-Based Optimization to Reduce Communication in Distributed Deep Learning"],"prefix":"10.1007","author":[{"given":"Wenbin","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"50_CR1","unstructured":"Dean, J., et al.: Large scale distributed deep networks. In: Proceedings of the 25th Conference on Neural Information Processing Systems (NIPS), pp. 1223\u20131231. MIT Press, Cambridge (2012)"},{"key":"50_CR2","unstructured":"Chen, T., et al.: MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems. In: Proceedings of the Workshop on Machine Learning Systems at the 28th Conference on Neural Information Processing Systems (LearningSys), pp. 1\u20136. MIT Press, Cambridge (2015)"},{"issue":"2","key":"50_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TBDATA.2015.2472014","volume":"1","author":"EP Xing","year":"2015","unstructured":"Xing, E.P., et al.: Petuum: a new platform for distributed machine learning on big data. IEEE Trans. Big Data 1(2), 49\u201367 (2015)","journal-title":"IEEE Trans. Big Data"},{"key":"50_CR4","unstructured":"Mamidala, A.R., Kollias, G., Ward, C., Artico, F.: MXNET-MPI: embedding MPI parallelism in parameter server task model for scaling deep learning. arXiv preprint arXiv:1801.03855 (2018)"},{"key":"50_CR5","unstructured":"Zhang, H., et al.: Poseidon: an efficient communication architecture for distributed deep learning on GPU clusters. In: Proceedings of the 2017 USENIX Annual Technical Conference (ATC), pp. 181\u2013193. USENIX Association, Berkeley (2017)"},{"issue":"3","key":"50_CR6","doi-asserted-by":"publisher","first-page":"32:1","DOI":"10.1145\/3005348","volume":"13","author":"S Anwar","year":"2017","unstructured":"Anwar, S., Hwang, K., Sung, W.: Structured pruning of deep convolutional neural networks. ACM J. Emerg. Technol. Comput. Syst. 13(3), 32:1\u201332:18 (2017)","journal-title":"ACM J. Emerg. Technol. Comput. Syst."},{"key":"50_CR7","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: Proceedings of the 5th International Conference on Learning Representations (ICLR). ICLR (2017)"},{"key":"50_CR8","doi-asserted-by":"crossref","unstructured":"Aji, A.F., Heafield, K.: Sparse communication for distributed gradient descent. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 440\u2013445. ACL, Stroudsburg (2017)","DOI":"10.18653\/v1\/D17-1045"},{"key":"50_CR9","doi-asserted-by":"crossref","unstructured":"Seide, F., Fu, H., Droppo, J., Li, G., Yu, D.: 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs. In: Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 1058\u20131062. ISCA (2014)","DOI":"10.21437\/Interspeech.2014-274"},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Strom, N.: Scalable distributed DNN training using commodity GPU cloud computing. In: Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 1488\u20131492. ISCA (2015)","DOI":"10.21437\/Interspeech.2015-354"},{"key":"50_CR11","unstructured":"Zhou, S., Ni, Z., Zhou, X., Wen, H., Wu, Y., Zou, Y.: DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Dryden, N., Moon, T., Jacobs, S.A., Essen, B.V.: Communication quantization for data-parallel training of deep neural networks. In: Proceedings of the 2nd Workshop on Machine Learning in HPC Environments (MLHPC), pp. 1\u20138. IEEE Computer Society, Los Alamitos (2016)","DOI":"10.1109\/MLHPC.2016.004"},{"key":"50_CR13","unstructured":"Lin, Y., Han, S., Mao, H., Wang, Y., Dally, B.: Deep gradient compression: reducing the communication bandwidth for distributed training. In: Proceedings of the 6th International Conference on Learning Representations (ICLR), pp. 1\u201314. ICLR (2018)"},{"key":"50_CR14","unstructured":"Huilgol, R.: 2bit gradient compression (2017). https:\/\/github.com\/apache\/incubator-mxnet\/pull\/8662"},{"key":"50_CR15","unstructured":"Wen, W., et al.: TernGrad: ternary gradients to reduce communication in distributed deep learning. In: Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS), pp. 1509\u20131519. MIT Press, Cambridge (2017)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60259-8_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T11:44:28Z","timestamp":1669203868000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60259-8_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030602581","9783030602598"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60259-8_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"16 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/apwebwaim2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"259","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was organized as a fully online conference.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}