RaiderSTREAM: Adapting the STREAM Benchmark to Modern HPC Systems

Abstract

Sustaining high memory bandwidth utilization is a common bottleneck to maximizing the performance of scien-tific applications, with the dominating factor of the runtime being the speed at which data can be loaded from memory into the CPU and results can be written back to memory, particularly for increasingly critical data-intensive workloads. The prevalence of irregular memory access patterns within these applications, exemplified by kernels such as those found in sparse matrix and graph applications, significantly degrade the achievable performance of a system's memory hierarchy. As such, it is highly desirable to be able to accurately measure a given memory hierarchy's sustainable memory bandwidth when designing applications as well as future high-performance computing (HPC) systems. STREAM is a de facto standard benchmark for measuring sustained memory bandwidth and has garnered widespread adoption. In this work, we discuss current limitations of the STREAM benchmark in the context of high-performance and scientific computing. We then introduce a new version of STREAM, called RaiderSTREAM, built on the OpenSHMEM and MPI programming models in tandem with OpenMP, that include additional kernels which better model irregular memory access patterns in order to address these shortcomings.