A Taxonomy of GPGPU Performance Scaling

Abstract

Graphics processing units (GPUs) range from small, embedded designs to large, high-powered discrete cards. While the performance of graphics workloads is generally understood, there has been little study of the performance of GPGPU applications across a variety of hardware configurations. This work presents performance scaling data gathered for 267 GPGPU kernels from 97 programs run on 891 hardware configurations of a modern GPU. We study the performance of these kernels across a 5× change in core frequency, 8.3× change in memory bandwidth, and 11× difference in compute units. We illustrate that many kernels scale in intuitive ways, such as those that scale directly with added computational capabilities or memory bandwidth. We also find a number of kernels that scale in non-obvious ways, such as losing performance when more processing units are added or plateauing as frequency and bandwidth are increased. In addition, we show that a number of current benchmark suites do not scale to modern GPU sizes, implying that either new benchmarks or new inputs are warranted.