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arXiv · 1702.06269

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox

Jialei Wang·Weiran Wang·Nathan Srebro
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Abstract

We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors). Our approach allows a communication-memory tradeoff, with either logarithmic communication but linear memory, or polynomial communication and a corresponding polynomial reduction in required memory. This communication-memory tradeoff is achieved through minibatch-prox iterations (minibatch passive-aggressive updates), where a subproblem on a minibatch is solved at each iteration. We provide a novel analysis for such a minibatch-prox procedure which achieves the statistical optimal rate regardless of minibatch size and smoothness, thus significantly improving on prior work.

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Jialei Wang, Weiran Wang, Nathan Srebro. 2017-02-21. Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox. https://arxiv.org/abs/1702.06269

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