Parameter Box: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training

Most work in the deep learning systems community has focused on faster inference, but arriving at a trained model requires lengthy experiments. Accelerating training lets developers iterate faster and come up with better models. DNN training is often seen as a compute-bound problem, best done in a single large compute node with many GPUs. As DNNs get bigger, training requires going distributed. Distributed deep neural network (DDNN) training constitutes an important workload on the cloud. Larger DNN models and faster compute engines shift the training performance bottleneck from computation to communication. Our experiments show existing DNN training frameworks do not scale in a typical cloud environment due to insufficient bandwidth and inefficient parameter server software stacks.We propose PBox, a balanced, scalable central PS hardware that balances compute and communication resources, and PHub, a high performance parameter server (PS) software design that provides an optimized network stack and a streamlined gradient processing pipeline to benefit common PS setups to utilize PBox. We show that in a typical cloud environment, PBox can achieve up to 3.8x speedup over state-of-the-art designs when training ImageNet. We discuss future directions of integrating PBox with programmable switches for in-network aggregation during training, leveraging the datacenter network topology to reduce bandwidth usage and localize data movement.