论文标题
分层屋顶线分析：如何使用Intel CPU和NVIDIA GPU上的性能工具收集数据
Hierarchical Roofline Analysis: How to Collect Data using Performance Tools on Intel CPUs and NVIDIA GPUs
论文作者
论文摘要
本文调查了一系列方法，以收集有关Intel CPU和NVIDIA GPU的必要性能数据进行分层屋顶分析。截至2020年中，英特尔顾问和Nvidia Nsight Compute两种供应商性能工具已将车顶线分析整合到其支持的功能集中。本文填补了这些工具不可用时或用户想要更自定义的工作流以进行某些分析时的空白。具体来说，我们将讨论如何在Intel Architectures上使用Intel Advisor，RRZE LIKWID，Intel SDE和Intel放大器，以及NVPROF，NSIGHT COMPUTE指标以及Nsight Compute Section Section consect Files在NVIDIA Architectures上。这些工具将用于收集尽可能多的内存/缓存级别的信息，以提供有关应用程序数据重用和缓存局部性特征的见解。
This paper surveys a range of methods to collect necessary performance data on Intel CPUs and NVIDIA GPUs for hierarchical Roofline analysis. As of mid-2020, two vendor performance tools, Intel Advisor and NVIDIA Nsight Compute, have integrated Roofline analysis into their supported feature set. This paper fills the gap for when these tools are not available, or when users would like a more customized workflow for certain analysis. Specifically, we will discuss how to use Intel Advisor, RRZE LIKWID, Intel SDE and Intel Amplifier on Intel architectures, and nvprof, Nsight Compute metrics, and Nsight Compute section files on NVIDIA architectures. These tools will be used to collect information for as many memory/cache levels in the memory hierarchy as possible in order to provide insights into application's data reuse and cache locality characteristics.
