The Approach Described Here Can Be Extended To Also Collect Gpu And Cpu Hardware Counters (Using Cupti And Papi) And To Support Mpi Applications.

This guide will walk early adopters through the steps on turning. My company currently ports our cuda based software from linux to windows. As a cuda developer, you will often need to control which devices your application uses.

It's Not All Just About Drivers And Apis;

Both make excellent parallel debuggers with extended support for cuda. If you are writing gpu enabled code, you would typically use a device query to select the desired gpus. I was looking for ways to reduce the wddm overhead of cuda kernel launches and found this article.

Does Your Cuda Application Need To Target A Specific Gpu?

This document describes nvidia profiling tools that enable you to understand and optimize the performance of your cuda, openacc or openmp applications. If it is less than 3.5, resolve won’t support your gpu card for versions after 16.2.7. When i execute device_lib.list_local_devices(), there is no gpu in the output.theano sees my gpu, and works fine with it, and examples in /usr/share/cuda/samples work fine as well.

It Can Be Used For Profiling And Tracing Applications In Order To Determine Bottlenecks.

Python virtual environments are a best practice for both python development and python deployment. Navigate to wikipedia gpu cuda support list. After locating your card, check the first column “compute capability (version)“.

I've Tried Tensorflow On Both Cuda 7.5 And 8.0, W/O Cudnn (My Gpu Is Old, Cudnn Doesn't Support It).

Because i have some custom jupyter image, and i want to base from that. The visual profiler is a graphical profiling tool that displays a timeline of your application's cpu and gpu activity, and that includes an automated analysis engine to identify optimization opportunities. My goal was to make a cuda enabled docker image without using nvidia/cuda as base image.