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 Caffe/Caffe2, TensorFlow, CNTK, Torch, and several others have emerged that offer ease of use and flexibility to describe, train, and deploy various types of Deep Neural Networks (DNN) including deep convolutional nets. In this tutorial, we will provide an overview of interesting trends in DL and how cutting-edge hardware architectures are playing a key role in moving the field forward. We will also present an overview of DL frameworks from an architectural as well as a performance standpoint. Most DL frameworks have utilized a single GPU to accelerate the performance of DNN training and inference. However, approaches to parallelizing the process of training are also being actively explored. The DL community has moved along MPI based parallel/distributed training as well. Thus, we will highlight new challenges for MPI runtimes to efficiently support DNN training. We highlight how we have designed efficient communication primitives in MVAPICH2 to support scalable DNN training. Finally, we will discuss how co-design of the OSU-Caffe framework and MVAPICH2 runtime enables scale-out of DNN training to 160 GPUs.

See http://web.cse.ohio-state.edu/~panda.2/ppopp18_dl_tut.html for more details