Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent

• URL: http://arxiv.org/abs/2001.05918v2
• Date: Sun, 28 Jun 2020 11:02:38 GMT
• Title: Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent
• Authors: Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
• Abstract summary: A key element behind the progress of machine learning in recent years has been the ability to train machine learning models in largescale distributed-memory environments. In this paper, we introduce general convergence methods used in practice to train large-scale machine learning models. Our framework, called elastic elastic bounds, enables us to derive convergence bounds for a variety of distributed SGD methods.
• Score: 28.006781039853575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning has made tremendous progress in recent years, with models matching or even surpassing humans on a series of specialized tasks. One key element behind the progress of machine learning in recent years has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Many of these models are trained employing variants of stochastic gradient descent (SGD) based optimization. In this paper, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. Our framework, called elastic consistency enables us to derive convergence bounds for a variety of distributed SGD methods used in practice to train large-scale machine learning models. The proposed framework de-clutters the implementation-specific convergence analysis and provides an abstraction to derive convergence bounds. We utilize the framework to analyze a sparsification scheme for distributed SGD methods in an asynchronous setting for convex and non-convex objectives. We implement the distributed SGD variant to train deep CNN models in an asynchronous shared-memory setting. Empirical results show that error-feedback may not necessarily help in improving the convergence of sparsified asynchronous distributed SGD, which corroborates an insight suggested by our convergence analysis.

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