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arXiv · 1508.05711

Fast Asynchronous Parallel Stochastic Gradient Decent

Shen-Yi Zhao·Wu-Jun Li
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Abstract

Stochastic gradient descent~(SGD) and its variants have become more and more popular in machine learning due to their efficiency and effectiveness. To handle large-scale problems, researchers have recently proposed several parallel SGD methods for multicore systems. However, existing parallel SGD methods cannot achieve satisfactory performance in real applications. In this paper, we propose a fast asynchronous parallel SGD method, called AsySVRG, by designing an asynchronous strategy to parallelize the recently proposed SGD variant called stochastic variance reduced gradient~(SVRG). Both theoretical and empirical results show that AsySVRG can outperform existing state-of-the-art parallel SGD methods like Hogwild! in terms of convergence rate and computation cost.

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Shen-Yi Zhao, Wu-Jun Li. 2015-08-24. Fast Asynchronous Parallel Stochastic Gradient Decent. https://arxiv.org/abs/1508.05711

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