  It is inevitable to train large deep learning models on a large-scale cluster
equipped with accelerators system. Deep gradient compression would highly
increase the bandwidth utilization and speed up the training process but hard
to implement on ring structure. In this paper, we find that redundant gradient
and gradient staleness has negative effect on training. We have observed that
in different epoch and different steps, the neural networks focus on updating
different layers and different parameters. In order to save more communication
bandwidth and preserve the accuracy on ring structure, which break the restrict
as the node increase, we propose a new algorithm to measure the importance of
gradients on large-scale cluster implementing ring all-reduce based on the size
of the ratio of parameter calculation gradient to parameter value. Our
importance weighted pruning approach achieved 64X and 58.8X of gradient
compression ratio on AlexNet and ResNet50 on ImageNet. Meanwhile, in order to
maintain the sparseness of the gradient propagation, we randomly broadcast the
index of important gradients on each node. While the remaining nodes are ready
for the index gradient and perform all-reduce update. This would speed up the
convergence of the model and preserve the training accuracy.
