BlueConnect: Decomposing all-reduce for deep learning on heterogeneous network hierarchy

Abstract

As deep neural networks get more complex and input datasets get larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, enabling distributed deep learning at a massive scale is critical since it offers the potential to reduce the training time from weeks to hours. In this article, we present BlueConnect, an efficient communication library for distributed deep learning that is highly optimized for popular GPU-based platforms. BlueConnect decomposes a single all-reduce operation into a large number of parallelizable reduce-scatter and all-gather operations to exploit the tradeoff between latency and bandwidth and adapt to a variety of network configurations. Therefore, each individual operation can be mapped to a different network fabric and take advantage of the best performing implementation for the corresponding fabric. According to our experimental results on two system configurations, BlueConnect can outperform the leading industrial communication library by a wide margin, and the BlueConnect-integrated Caffe2 can significantly reduce synchronization overhead by 87% on 192 GPUs for Resnet-50 training over prior schemes.