Predicting Cross-Architecture Performance of Parallel Programs

A variety of hardware architectures, both CPUs and GPUs, are used today to build supercomputers and parallel clusters. Often times, users can choose which hardware platform they want to run on. Modern scientific workflows have multiple computational tasks, and each task may be better suited for a different architecture in terms of performance. Deciding where to run an application or workflow task is not straightforward because of the complexity of applications, and hardware architectures, which makes performance predictions challenging. Hence, modeling the performance of scientific applications across a variety of architectures is important for achieving the best performance. In this paper, we present a machine learning based methodology to model the relative performance of applications across multiple architectures using hardware performance counters. Our machine learning model can predict the relative performance of an application with a mean absolute error of 0.11, and can be used effectively to make performance-aware and multi-architecture scheduling decisions, reducing makespan by up to 20%.