Revisiting Distributed Synchronous SGD
Venue
International Conference on Learning Representations Workshop Track (2016) (to appear)
Publication Year
2016
Authors
Jianmin Chen, Rajat Monga, Samy Bengio, Rafal Jozefowicz
BibTeX
Abstract
The recent success of deep learning approaches for domains like speech recognition
(Hinton et al., 2012) and computer vision (Ioffe & Szegedy, 2015) stems from
many algorithmic improvements but also from the fact that the size of available
training data has grown significantly over the years, together with the computing
power, in terms of both CPUs and GPUs. While a single GPU often provides
algorithmic simplicity and speed up to a given scale of data and model, there exist
an operating point where a distributed implementation of training algorithms for
deep architectures becomes necessary. Previous works have been focusing on
asynchronous SGD training, which works well up to a few dozens of workers for some
models. In this work, we show that synchronous SGD training, with the help of
backup workers, can not only achieve better accuracy, but also reach convergence
faster with respect to wall time, i.e. use more workers more efficiently.
