  Distributed synchronous stochastic gradient descent has been widely used to
train deep neural networks on computer clusters. With the increase of
computational power, network communications have become one limiting factor on
system scalability. In this paper, we observe that many deep neural networks
have a large number of layers with only a small amount of data to be
communicated. Based on the fact that merging some short communication tasks
into a single one may reduce the overall communication time, we formulate an
optimization problem to minimize the training iteration time. We develop an
optimal solution named merged-gradient WFBP (MG-WFBP) and implement it in our
open-source deep learning platform B-Caffe. Our experimental results on an
8-node GPU cluster with 10GbE interconnect and trace-based simulation results
on a 64-node cluster both show that the MG-WFBP algorithm can achieve much
better scaling efficiency than existing methods WFBP and SyncEASGD.
