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dc.contributor.authorDutta, Aritra
dc.contributor.authorBergou, El Houcine
dc.contributor.authorAbdelmoniem, Ahmed M.
dc.contributor.authorHo, Chen-Yu
dc.contributor.authorSahu, Atal Narayan
dc.contributor.authorCanini, Marco
dc.contributor.authorKalnis, Panos
dc.date.accessioned2019-11-19T09:36:56Z
dc.date.available2019-11-19T09:36:56Z
dc.date.issued2019-11
dc.identifier.urihttp://hdl.handle.net/10754/660127
dc.description.abstractCompressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks. However, there exists a discrepancy between theory and practice: while theoretical analysis of most existing compression methods assumes compression is applied to the gradients of the entire model, many practical implementations operate individually on the gradients of each layer of the model. In this paper, we prove that layer-wise compression is, in theory, better, because the convergence rate is upper bounded by that of entire-model compression for a wide range of biased and unbiased compression methods. However, despite the theoretical bound, our experimental study of six well-known methods shows that convergence, in practice, may or may not be better, depending on the actual trained model and compression ratio. Our findings suggest that it would be advantageous for deep learning frameworks to include support for both layer-wise and entire-model compression.
dc.relation.urlhttps://github.com/sands-lab/layer-wise-aaai20
dc.subjectdistributed deep learning
dc.subjectgradient compression
dc.subjectgradient sparsification
dc.subjectgradient quantization
dc.subjectlayer-wise gradient compression
dc.titleOn the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
dc.typeTechnical Report
dc.contributor.departmentCEMSE
kaust.personDutta, Aritra
kaust.personBergou, El Houcine
kaust.personAbdelmoniem, Ahmed M.
kaust.personHo, Chen-Yu
kaust.personSahu, Atal Narayan
kaust.personCanini, Marco
kaust.personKalnis, Panos
refterms.dateFOA2019-11-19T00:00:00Z


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