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Parallel Gradient Descent for Multilayer Feedforward Neural Networks

We present a parallel approach to classification using neural networks as the hypothesis class. Neural networks can have millions of parameters and learning the optimum value of all parameters from huge datasets in a serial implementation can be a very time consuming task. In this work, we have implemented parallel gradient descent to train multilayer feedforward neural networks. Specifically, we analyze two kinds of parallelization techniques: (a) parallel processing of multiple training examples across several threads and (b) parallelizing matrix operations for a single training example. We have implemented a serial minibatch gradient descent algorithm, its parallel multithreaded version (using Pthread library in C++), a BLAS parallelized version and a CUDA implementation on a GPU. All implementations have been compared and analyzed for the speedup obtained across various network architectures and increasing problem sizes. We have performed our tests on the benchmark dataset: MNIST, and finally also compared our implementations with the corresponding implementations in the state-ofthe-art deep learning library: Theano.