Data parallel large sparse deep neural network on GPU

Sparse Deep Neural Network (DNN) is an emerging research area since deploying deep neural networks with limited resources is very challenging. In this work, we provide a scalable solution to the Sparse DNN Challenge-a challenge posed by MIT/IEEE/Amazon GraphChallenge.org-by designing data parallelism on GPUs. We provide a solution based on Python TensorFlow as it is a widely used tool in different scientific applications for deep learning. We use the datasets provided by GraphChallenge, derived from the MNIST handwritten letters. We use the Synthetic DNNs from RadiX-Net with varying number of neurons and layers. We implement a data parallel implementation of Sparse DNN using TensorFlow on GPU. Our solution shows up to 4.7× speedup over the basehne serial MATLAB implementation given in GraphChallenge. In addition to that, our TensorFlow GPU implementation demonstrates a 3-fold speedup over our TensorFloW CPU implementation.