On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Bergou, El Houcine
Abdelmoniem, Ahmed M.
Sahu, Atal Narayan
KAUST DepartmentComputer Science
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
InfoCloud Research Group
Preprint Posting Date2019-11-19
Permanent link to this recordhttp://hdl.handle.net/10754/660127
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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.
CitationDutta, A., Bergou, E. H., Abdelmoniem, A. M., Ho, C.-Y., Sahu, A. N., Canini, M., & Kalnis, P. (2020). On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3817–3824. doi:10.1609/aaai.v34i04.5793