GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine; Hailin Jin; Jianchao Yang; Zhe Lin; Thomas Huang

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arXiv · 1312.6186

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine·Hailin Jin·Jianchao Yang·Zhe Lin·Thomas Huang

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Abstract

The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been used mostly in industry. We report early experiments with a system that makes use of both model parallelism and data parallelism, we call GPU A-SGD. We show using GPU A-SGD it is possible to speed up training of large convolutional neural networks useful for computer vision. We believe GPU A-SGD will make it possible to train larger networks on larger training sets in a reasonable amount of time.

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BibTeX RIS

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang. 2013-12-21. GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training. https://arxiv.org/abs/1312.6186

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