Fast Distributed Deep Learning via Worker-adaptive Batch Sizing

Deep neural network models are usually trained in cluster environments, where the model parameters are iteratively refined by multiple worker machines in parallel. One key challenge in this regard is the presence of stragglers, which significantly degrades the learning performance. In this paper, we propose to eliminate stragglers by adapting each worker's training load to its processing capability; that is, slower workers receive a smaller batch of data to process. Following this idea, we develop a new synchronization scheme called LB-BSP (Load-balanced BSP). It works by coordinately setting the batch size of each worker so that they can finish batch processing at around the same time. A prerequisite for deciding the workers' batch sizes is to know their processing speeds before each iteration starts. For the best prediction accuracy, we adopt NARX, an extended recurrent neural network that accounts for both the historical speeds and the driving factors such as CPU and memory in prediction. We have implemented LB-BSP for both TensorFlow and MXNet. EC2 experiments against popular benchmarks show that LB-BSP can effectively accelerate the training of deep models, with up to 2x speedup.