Microbenchmarks for GPU Characteristics: The Occupancy Roofline and the Pipeline Model

J. Lemeire, Jan G. Cornelis, Laurent Segers
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引用次数: 8

Abstract

In this paper we present microbenchmarks in OpenCL to measure the most important performance characteristics of GPUs. Microbenchmarks try to measure individual characteristics that influence the performance. First, performance, in operations or bytes per second, is measured with respect to the occupancy and as such provides an occupancy roofline curve. The curve shows at which occupancy level peak performance is reached. Second, when considering the cycles per instruction of each compute unit, we measure the two most important characteristics of an instruction: its issue and completion latency. This is based on modeling each compute unit as a pipeline for computations and a pipeline for the memory access. We also measure some specific characteristics: the influence of independent instructions within a kernel and thread divergence. We argue that these are the most important characteristics for understanding the performance and predicting performance. The results for several Nvidia and AMD GPUs are provided. A free java application containing the microbenchmarks is available on www.gpuperformance.org.
GPU特性的微基准测试:占用屋顶线和管道模型
在本文中,我们在OpenCL中提出了微基准测试来测量gpu最重要的性能特征。微基准测试试图测量影响性能的单个特征。首先,性能(以操作数或每秒字节数为单位)是根据占用率来衡量的,这样就提供了占用率曲线。曲线显示了达到占用水平的峰值性能。其次,在考虑每个计算单元的每条指令的周期时,我们测量指令的两个最重要的特征:它的发出和完成延迟。这是基于将每个计算单元建模为用于计算的管道和用于内存访问的管道。我们还测量了一些特定的特征:内核内独立指令的影响和线程的发散。我们认为这些是理解性能和预测性能的最重要的特征。给出了几种Nvidia和AMD gpu的测试结果。包含微基准测试的免费java应用程序可在www.gpuperformance.org上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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