High performance distributed deep learning

Abstract

The current wave of advances in Deep Learning (DL) has led to many exciting challenges and opportunities for Computer Science and Artificial Intelligence researchers alike. Modern DL frameworks like Caffe2, TensorFlow, Cognitive Toolkit (CNTK), PyTorch, and several others have emerged that offer ease of use and flexibility to describe, train, and deploy various types of Deep Neural Networks (DNNs). In this tutorial, we will provide an overview of interesting trends in DNN design and how cutting-edge hardware architectures are playing a key role in moving the field forward. We will also present an overview of different DNN architectures and DL frameworks. Most DL frameworks started with a single-node/single-GPU design. However, approaches to parallelize the process of DNN training are also being actively explored. The DL community has moved along different distributed training designs that exploit communication runtimes like gRPC, MPI, and NCCL. In this context, we will highlight new challenges and opportunities for communication runtimes to efficiently support distributed DNN training. We also highlight some of our co-design efforts to utilize CUDA-Aware MPI for large-scale DNN training on modern GPU clusters. Finally, we include hands-on exercises in this tutorial to enable the attendees to gain first-hand experience of running distributed DNN training experiments on a modern GPU cluster.