Performance Analysis of Deep Learning Workloads on a Composable System

Abstract

A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix and match' resources dynamically allows for experimentation early in the design cycle, prior to the final architectural design or hardware implementation of a system. We describe the design of an enterprise composable infrastructure that we have implemented and evaluate the impact of resource dis-aggregation on representative deep learning benchmarks.