{"volume":"Volume 2018","year":"2018","isi":1,"project":[{"call_identifier":"H2020","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program"}],"page":"5973-5983","publication":"Advances in Neural Information Processing Systems 31","author":[{"orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Hoefler","first_name":"Torsten","full_name":"Hoefler, Torsten"},{"full_name":"Johansson, Mikael","last_name":"Johansson","first_name":"Mikael"},{"id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","full_name":"Konstantinov, Nikola H","last_name":"Konstantinov","first_name":"Nikola H"},{"full_name":"Khirirat, Sarit","last_name":"Khirirat","first_name":"Sarit"},{"full_name":"Renggli, Cedric","last_name":"Renggli","first_name":"Cedric"}],"external_id":{"isi":["000461852000047"],"arxiv":["1809.10505"]},"ec_funded":1,"conference":{"location":"Montreal, Canada","end_date":"2018-12-08","name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2018-12-02"},"citation":{"mla":"Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” <i>Advances in Neural Information Processing Systems 31</i>, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.","ieee":"D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in <i>Advances in Neural Information Processing Systems 31</i>, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.","ama":"Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: <i>Advances in Neural Information Processing Systems 31</i>. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.","ista":"Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.","apa":"Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., &#38; Renggli, C. (2018). The convergence of sparsified gradient methods. In <i>Advances in Neural Information Processing Systems 31</i> (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.","chicago":"Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In <i>Advances in Neural Information Processing Systems 31</i>, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.","short":"D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural Information Processing Systems Foundation, 2018, pp. 5973–5983."},"language":[{"iso":"eng"}],"_id":"6589","date_created":"2019-06-27T09:32:55Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"oa_version":"Preprint","scopus_import":"1","publisher":"Neural Information Processing Systems Foundation","status":"public","title":"The convergence of sparsified gradient methods","quality_controlled":"1","day":"01","type":"conference","date_updated":"2023-10-17T11:47:20Z","month":"12","article_processing_charge":"No","date_published":"2018-12-01T00:00:00Z","publication_status":"published","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1809.10505"}],"abstract":[{"lang":"eng","text":"Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of communication-reduction methods, such as quantization, large-batch methods, and gradient sparsification, have been proposed. To date, gradient sparsification methods--where each node sorts gradients by magnitude, and only communicates a subset of the components, accumulating the rest locally--are known to yield some of the largest practical gains. Such methods can reduce the amount of communication per step by up to \\emph{three orders of magnitude}, while preserving model accuracy. Yet, this family of methods currently has no theoretical justification. This is the question we address in this paper. We prove that, under analytic assumptions, sparsifying gradients by magnitude with local error correction provides convergence guarantees, for both convex and non-convex smooth objectives, for data-parallel SGD. The main insight is that sparsification methods implicitly maintain bounds on the maximum impact of stale updates, thanks to selection by magnitude. Our analysis and empirical validation also reveal that these methods do require analytical conditions to converge well, justifying existing heuristics."}],"department":[{"_id":"DaAl"},{"_id":"ChLa"}]}