  Communication in parallel systems imposes significant overhead which often
turns out to be a bottleneck in parallel machine learning. To relieve some of
this overhead, in this paper, we present EventGraD - an algorithm with
event-triggered communication for stochastic gradient descent in parallel
machine learning. The main idea of this algorithm is to modify the requirement
of communication at every iteration in standard implementations of stochastic
gradient descent in parallel machine learning to communicating only when
necessary at certain iterations. We provide theoretical analysis of convergence
of our proposed algorithm. We also implement the proposed algorithm for
data-parallel training of a popular residual neural network used for training
the CIFAR-10 dataset and show that EventGraD can reduce the communication load
by up to 60% while retaining the same level of accuracy. In addition, EventGraD
can be combined with other approaches such as Top-K sparsification to decrease
communication further while maintaining accuracy.
