CUDA"

Installation

This Howto provides a way to install the official NVIDIA packages for CUDA.

Repository

This repository contains a given version of CUDA that is parallel installable along with another version.

Please use the Official link: https://developer.nvidia.com/cuda-downloads

• RHEL/CentOS 7

  sudo yum install http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-9.0.176-1.x86_64.rpm
  sudo yum install cuda

• RHEL/CentOS 6

  sudo yum install http://developer.download.nvidia.com/compute/cuda/repos/rhel6/x86_64/cuda-repo-rhel6-9.0.176-1.x86_64.rpm
  sudo yum install cuda

• Fedora 25 (and later)

  sudo dnf install http://developer.download.nvidia.com/compute/cuda/repos/fedora25/x86_64/cuda-repo-fedora25-9.0.176-1.x86_64.rpm
  sudo dnf install cuda

Legacy NVIDIA-340XX/CUDA

This repository contains a legacy version of CUDA 6.5 that will works with the NVIDIA 340xx serie

Please use the Official link: https://developer.nvidia.com/cuda-toolkit-65

• RHEL/CentOS 6

  sudo yum install http://developer.download.nvidia.com/compute/cuda/repos/rhel6/x86_64/cuda-repo-rhel6-6.5-14.x86_64.rpm
  sudo yum install cuda

• Fedora 20 (and later)

  sudo yum install install http://developer.download.nvidia.com/compute/cuda/repos/fedora20/x86_64/cuda-repo-fedora20-6.5-14.x86_64.rpm
  sudo yum install cuda

Please verify to have a compatible compiler.

Known issues

GCC version

When using a later version of Fedora than what is supported by the NVIDIA CUDA Official repository, you might be unable to compile. You can either:

• Tweak the /usr/local/cuda-9.0/targets/x86_64-linux/include/host_defines.h to accept the Fedora default compiler.

• Install the appropriate gcc version for CentOS developper toolset. Please see https://www.softwarecollections.org/en/scls/rhscl/devtoolset-6/

sudo dnf install http://ftp.ciril.fr/pub/linux/centos/7.4.1708/extras/x86_64/Packages/centos-release-scl-2-2.el7.centos.noarch.rpm
sudo dnf install devtoolset-6-toolchain

You cannot install the whole devtoolset-6 collection, but the toolchain is enough , then each time you need to build using cuda, you start by

scl run devtoolset-6 bash
gcc --version
gcc (GCC) 6.3.1 20170216 (Red Hat 6.3.1-3)
Copyright (C) 2016 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
exit
gcc --version
gcc (GCC) 7.2.1 20170915 (Red Hat 7.2.1-2)
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Which driver Package

Both "CUDA" and "RPM Fusion" repositories provide the nvidia driver packages. Usually, the package provided by RPM Fusion is higher. But in case you want to avoid the risk, add this:

#/etc/yum.repos.d/cuda.repo
[cuda]
name=cuda
...
exclude=xorg-x11-drv-nvidia\*,akmod-nvidia\*

NVIDIA provided libOpenCL

NVIDIA only advertise OpenCL 1.2 with the binary driver at this time. As a consequence, they provide an old version of libOpenCL.so.1 which works fine with their binary driver. As most software in Fedora and RPM Fusion are built using a newer libOpenCL, the system linker detects that and issues the following message:

 /usr/local/cuda-9.0/targets/x86_64-linux/lib/libOpenCL.so.1: no version information available (required by ffmpeg)

You can either ignore the message or manually delete the libOpenCL.so.1 provided by NVIDIA (run sudo ldconfig once deleted). Please verify to not have other OpenCL providers that might interfere with NVIDIA OpenCL usage. (looking at /etc/OpenCL/vendors ).

Running blender

Even when only running blender, you need a CUDA compatible compiler as described above. This is because blender will compile the "CUDA Kernels" optimized for your own GPU. You can run blender with:

 scl run devtoolset-6 blender

Once the "CUDA kernels" are compiled, you can run blender normally

References

• CUDA Start quide: https://developer.nvidia.com/compute/cuda/9.0/prod/docs/sidebar/CUDA_Quick_Start_Guide-pdf

• CUDA documentation: https://docs.nvidia.com/cuda/index.html

CategoryHowto