  The state-of-the-art deep neural networks (DNNs) have significant
computational and data management requirements. The size of both training data
and models continue to increase. Sparsification and pruning methods are shown
to be effective in removing a large fraction of connections in DNNs. The
resulting sparse networks present unique challenges to further improve the
computational efficiency of training and inference in deep learning. Both the
feedforward (inference) and backpropagation steps in stochastic gradient
descent (SGD) algorithm for training sparse DNNs involve consecutive sparse
matrix-vector multiplications (SpMVs). We first introduce a distributed-memory
parallel SpMV-based solution for the SGD algorithm to improve its scalability.
The parallelization approach is based on row-wise partitioning of weight
matrices that represent neuron connections between consecutive layers. We then
propose a novel hypergraph model for partitioning weight matrices to reduce the
total communication volume and ensure computational load-balance among
processors. Experiments performed on sparse DNNs demonstrate that the proposed
solution is highly efficient and scalable. By utilizing the proposed matrix
partitioning scheme, the performance of our solution is further improved
significantly.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing