Accelerating Massively Distributed Deep Learning Through Efficient Pseudo-Synchronous Update Method

In recent years, deep learning models have been successfully applied to large-scale data analysis, including image classification, video caption, natural language processing, etc. Large-scale data analyses take advantage of parallel computing to accelerate the speed of model training, in which data parallelism has become the dominant method for deep learning model training due to its high throughput rate. Synchronous stochastic gradient descent optimization becomes a well-recognized optimization method to ensure model convergence, but the overhead of gradients synchronization increases linearly as the number of workers increases, causing a huge waste of time. Although some efficiency-first asynchronous methods have been proposed, these methods cannot guarantee their convergence in large-scale distributed training. To solve this problem, we propose an efficient pseudo-synchronous approach that updates the network with the previous gradient, performing the synchronization of a new gradient to overlap computation and synchronization. This idea will obviously affect the normal convergence of the model, so we propose a novel adaptive exponential smoothing predicted gradient algorithm for model optimization, which can adaptively adjust the confidence coefficient of the history gradient to ensure the normal convergence of the training process. Experiments prove that our method can speed up the training process and achieve a comparable accuracy rate with standard synchronous SGD. Besides, our method has more efficient weak scalability compared to the traditional synchronous SGD and those in previous related work. We apply our methods to image recognition and video caption applications at most 12288 cores with strong scalability on Tianhe II. Evaluations show that, when configured appropriately, our method attains near-linear scalability using 128 nodes. We get 93.4% weak scaling efficiency on 64 nodes, 90.5% on 128 nodes.