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The next step is to install the required packages, and updates the kernel if needed. On Fedora, you can use yum update -l all. If you use yum as the package manager, it will also ensure you have the development packages for the kernel being used. You can use dpkg as the package manager, and do the same.
Cg binaries are available from the NVIDIA drivers page. Once you have installed and configured the NVIDIA driver, you need to ensure the CUDA Driver is enabled and working by going to the NVIDIA CUDA Downloads page . The CUDA Runtime API, the CUDA Driver API and the CUDA runtime are all provided under the CUDA Driver. It is also advisable to download and install the latest version of NVIDIA CUDA samples for both the CUDA runtime and CUDA Driver API. The latest samples can be found at here .
Next we need to copy the library files to the local system. This will allow our CUDA programming to work. We use cp to copy the CUDA libraries and the CUDA runtime. For example, if you want to copy the CUDA runtime library files into /usr/local/cuda-8.0/lib, you would type the following:
Edit the file /etc/sysconfig/gpu and ensure you have the correct version of NVIDIA's Cg driver module installed for the GPU in your system. The CUDA Driver API will use this file to configure the CUDA Driver. The gpu variable is used by the CUDA Driver API to identify which GPU device to use (the gpu is a string which contains the name of the GPU as well as other information).
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One of the drawbacks to this approach was that we needed to recompile every time we made even a small change to the code. This slowed everything else we were doing down and because we couldn't always have someone waiting for the application to finish building. 827ec27edc