Theoretical Scalability Analysis of Distributed Deep Convolutional Neural Networks

Abstract

We analyze the asymptotic performance of the training process of deep neural networks (NN) on clusters in order to determine the scalability. For this purpose, i) we assume a data parallel implementation of the training algorithm, which distributes the batches among the cluster nodes and replicates the model; ii) we leverage the roofline model to inspect the performance at the node level, taking into account the floating-point unit throughput and memory bandwidth; and iii) we consider distinct collective communication schemes that are optimal depending on the message size and underlying network interconnection topology. We then apply the resulting performance model to analyze the scalability of several well-known deep convolutional neural networks as a function of the batch size, node floating-point throughput, node memory bandwidth, cluster dimension, and link bandwidth.