  This work examines the performance of leading-edge systems designed for
machine learning computing, including the NVIDIA DGX-2, Amazon Web Services
(AWS) P3, IBM Power System Accelerated Compute Server AC922, and a
consumer-grade Exxact TensorEX TS4 GPU server. Representative deep learning
workloads from the fields of computer vision and natural language processing
are the focus of the analysis. Performance analysis is performed along with a
number of important dimensions. Performance of the communication interconnects
and large and high-throughput deep learning models are considered. Different
potential use models for the systems as standalone and in the cloud also are
examined. The effect of various optimization of the deep learning models and
system configurations is included in the analysis.
