有没有一种方法可以分析OpenCL或pyOpenCL程序? [英] Is there a way to profile an OpenCL or a pyOpenCL program?

问题描述

我正在尝试优化pyOpenCL程序.出于这个原因，我想知道是否有一种方法可以对程序进行概要分析，并了解大部分时间在哪里.

I am trying to optimize a pyOpenCL program. For this reason I was wondering if there is a way to profile the program and see where most of the time is needed for.

您知道如何解决此问题吗?

Do you have any idea how to approach this problem?

预先感谢
安迪

Thanks in advance
Andi

例如，用于CUDA的nvidias nvprof可以解决pyCuda的问题，但不能解决pyOpenCL.

For example nvidias nvprof for CUDA would do the trick for pyCuda, however, not for pyOpenCL.

推荐答案

好的，
我想出了一种方法:Cuda Toolkit 3.1提供了针对openCL的配置文件(更高版本不会提供).在此程序包中，使用compute visual profiler(即(computeprof.exe)).它可在Windows和Linux 此处使用，并且可以与新的Cuda一起安装工具包.
看起来像这样:

Ok,
I have figured out a way: The Cuda Toolkit 3.1 offers a profiling for openCL (higher versions will not). From this package use the compute visual profiler which is the (computeprof.exe). It is available for windows and linux here and can be installed alongside a new Cuda Toolkit.
It looks like this:

我希望这也会对其他人有所帮助.

I hope this helps someone else too.

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