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

机译:GPU特性的微基准测试:占用天窗和管道模型

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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.
机译:在本文中,我们介绍了OpenCL中的微基准测试,以测量GPU的最重要的性能特征。微基准测试试图衡量影响性能的各个特征。首先,相对于占用率来衡量性能(以每秒操作数或字节数为单位),从而提供占用率屋顶曲线。该曲线显示达到哪个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,我们测量一条指令的两个最重要的特征:其发出和完成等待时间。这是基于将每个计算单元建模为用于计算的流水线和用于存储器访问的流水线的。我们还测量了一些特定的特征:内核中独立指令的影响和线程分歧。我们认为,这些是了解性能和预测性能的最重要特征。提供了几个Nvidia和AMD GPU的结果。包含微基准的免费Java应用程序可在www.gpuperformance.org上获得。

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