Data Parallelism in Training Sparse Neural Networks

Network pruning is an effective methodology to compress large neural networks, and sparse neural networks obtained by pruning can benefit from their reduced memory and computational costs at use. Notably, recent advances have found that it is possible to find a trainable sparse neural network even at random initialization prior to training; hence the obtained sparse network only needs to be trained. While this approach of pruning at initialization turned out to be highly effective, little has been studied about the training aspects of these sparse neural networks. In this work, we focus on measuring the effects of data parallelism on training sparse neural networks. As a result, we find that the data parallelism in training sparse neural networks is no worse than that in training densely parameterized neural networks, despite the general difficulty of training sparse neural networks. When training sparse networks using SGD with momentum, the breakdown of the perfect scaling regime occurs even much later than the dense at large batch sizes.