  Training time on large datasets for deep neural networks is the principal
workflow bottleneck in a number of important applications of deep learning,
such as object classification and detection in automatic driver assistance
systems (ADAS). To minimize training time, the training of a deep neural
network must be scaled beyond a single machine to as many machines as possible
by distributing the optimization method used for training. While a number of
approaches have been proposed for distributed stochastic gradient descent
(SGD), at the current time synchronous approaches to distributed SGD appear to
be showing the greatest performance at large scale. Synchronous scaling of SGD
suffers from the need to synchronize all processors on each gradient step and
is not resilient in the face of failing or lagging processors. In asynchronous
approaches using parameter servers, training is slowed by contention to the
parameter server. In this paper we compare the convergence of synchronous and
asynchronous SGD for training a modern ResNet network architecture on the
ImageNet classification problem. We also propose an asynchronous method,
gossiping SGD, that aims to retain the positive features of both systems by
replacing the all-reduce collective operation of synchronous training with a
gossip aggregation algorithm. We find, perhaps counterintuitively, that
asynchronous SGD, including both elastic averaging and gossiping, converges
faster at fewer nodes (up to about 32 nodes), whereas synchronous SGD scales
better to more nodes (up to about 100 nodes).
