  Data parallelism has become the de facto standard for training Deep Neural
Network on multiple processing units. In this work we propose DC-S3GD, a
decentralized (without Parameter Server) stale-synchronous version of the
Delay-Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm.
In our approach, we allow for the overlap of computation and communication, and
compensate the inherent error with a first-order correction of the gradients.
We prove the effectiveness of our approach by training Convolutional Neural
Network with large batches and achieving state-of-the-art results.
