Getting Started Linux (PDF) - v6.0 (older) - Last updated February 13, 2014 - Send Feedback -

## NVIDIA CUDA Getting Started Guide for Linux

## 1. Introduction

CUDA™ is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

CUDA was developed with several design goals in mind:

- Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C⁠+⁠+, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.
- Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.

CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.

This guide will show you how to install and check the correct operation of the CUDA development tools.

### 1.1. System Requirements

To use CUDA on your system, you will need the following installed:

- CUDA-capable GPU
- A supported version of Linux with a gcc compiler and toolchain
- NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)

The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

Table 1. x86 Linux Distribution Support in CUDA 6.0
Distribution | x86_64 | ARM | Kernel | GCC | GLIBC | ICC
--- | --- | --- | --- | --- | --- | ---
Fedora 19 | YES | NO | 3.9.9 | 4.8.1 | 2.17 | 13.1
CentOS 6.4 | YES | NO | 2.6.32 | 4.4.7 | 2.12
CentOS 5.5+ | YES | NO | 2.6.18 | 4.1.2 | 2.5
OpenSUSE 12.3 | YES | NO | 3.7.10 | 4.7.2 | 2.17
Red Hat Enterprise Linux (RHEL) 6.x | YES | NO | 2.6.32 | 4.4.7 | 2.12
RHEL 5.5+ | YES | NO | 2.6.18 | 4.1.2 | 2.5
SUSE SLES 11 SP3 | YES | NO | 3.0.76 | 4.3.4 | 2.11.3
SUSE SLES 11 SP2 | YES | NO | 3.0.13 | 4.3.4 | 2.11.3
Ubuntu 13.04 | YES | YES | 3.8.0 | 4.7.3 | 2.17
Ubuntu 12.04 | YES* | YES | 3.2.0 | 4.6 | 2.15
L4T r19.1 | NO | YES | 3.10 | 4.7.3 | 2.17

* Only Ubuntu 12.04 on x86 supports armhf cross development.

### 1.2. About This Document

This document is intended for readers familiar with the Linux environment and the compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation. Note: This guide covers installation only on systems with X Windows installed.

Note: Many commands in this document might require superuser privileges. On most distributions of Linux, this will require you to log in as root. For systems that have enabled the sudo package, use the sudo prefix for all necessary commands.

## 2. Pre-installation Actions

Some actions must be taken before the CUDA Toolkit and Driver can be installed on Linux:

- Verify the system has a CUDA-capable GPU.
- Verify the system is running a supported version of Linux.
- Verify the system has gcc installed.
- Download the NVIDIA CUDA Toolkit.

Note: You can override the install-time prerequisite checks by running the installer with the -override flag. Remember that the prerequisites will still be required to use the NVIDIA CUDA Toolkit.

### 2.1. Verify You Have a CUDA-Capable GPU

To verify that your GPU is CUDA-capable, go to your distribution's equivalent of System Properties, or, from the command line, enter:

    lspci | grep -i nvidia

If you do not see any settings, update the PCI hardware database that Linux maintains by entering `update-pciids` (generally found in `/sbin`) at the command line and rerun the previous `lspci` command.

If your graphics card is from NVIDIA and it is listed in http://developer.nvidia.com/cuda-gpus, your GPU is CUDA-capable.

The Release Notes for the CUDA Toolkit also contain a list of supported products.

### 2.2. Verify You Have a Supported Version of Linux

The CUDA Development Tools are only supported on some specific distributions of Linux. These are listed in the CUDA Toolkit release notes.

To determine which distribution and release number you're running, type the following at the command line:

    uname -m && cat /etc/*release

You should see output similar to the following, modified for your particular system:

    i386 Red Hat Enterprise Linux WS release 4 (Nahant Update 6)

The `i386` line indicates you are running on a 32-bit system. On 64-bit systems running in 64-bit mode, this line will generally read: `x86_64`. The second line gives the version number of the operating system.

### 2.3. Verify the System Has gcc Installed

The `gcc` compiler and toolchain generally are installed as part of the Linux installation, and in most cases the version of gcc installed with a supported version of Linux will work correctly.

To verify the version of gcc installed on your system, type the following on the command line:

    gcc --version

If an error message displays, you need to install the development tools from your Linux distribution or obtain a version of `gcc` and its accompanying toolchain from the Web.

For ARMv7 cross development, a suitable cross compiler is required. For example, performing the following on Ubuntu 12.04:

    sudo apt-get install g++-4.6-arm-linux-gnueabihf

will install the gcc 4.6 cross compiler on your system which will be used by `nvcc`. Please refer to th NVCC manual on how to use `nvcc` to cross compile to the ARMv7 architecture

### 2.4. Choose an Installation Method

The CUDA Toolkit can be installed using either of two different installation mechanisms: distribution-specific packages, or a distribution-independent package. The distribution-independent package has the advantage of working across a wider set of Linux distributions, but does not update the distribution's native package management system. The distribution-specific packages interface with the distribution's native package management system. It is recommended to use the distribution-specific packages, where possible.

Note: Distribution-specific packages and repositories are not provided for Redhat 5 and Ubuntu 10.04. For those two Linux distributions, the stand-alone installer must be used.
Note: Standalone installers are not provided for the ARMv7 release. For both native ARMv7 as well as cross developent, the toolkit must be installed using the distribution-specific installer.

### 2.5. Download the NVIDIA CUDA Toolkit

The NVIDIA CUDA Toolkit is available at http://developer.nvidia.com/cuda-downloads.

Choose the platform you are using and download the NVIDIA CUDA Toolkit

The CUDA Toolkit contains the CUDA driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples source code, and other resources.

### Download Verification

The download can be verified by comparing the MD5 checksum posted at http://developer.nvidia.com/cuda-downloads/checksums with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

To calculate the MD5 checksum of the downloaded file, run the following:

    $ md5sum <file>

## 3. Package Manager Installation

The installation is a two-step process. First the small repository configuration package must be downloaded from the NVIDIA CUDA download page, and installed manually. The package sets the package manager database to include the CUDA repository. Then the CUDA Toolkit is installed using the package manager software.

### 3.1. Prerequisites

- The package manager installations (RPM/DEB packages) and the stand-alone installer installations (.run file) of the NVIDIA driver are incompatible. Before using the distribution-specific packages, uninstall the NVIDIA driver with the following command:

      sudo /usr/bin/nvidia-uninstall

- For x86 to ARMv7 cross development, the host system must be an Ubuntu 12.04 system.

### 3.2. Redhat/CentOS

1. Perform the pre-installation actions.
2. Satisfy DKMS dependency

   The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau. Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

3. Address custom xorg.conf, if applicable

   The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf. If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

4. Install repository meta-data

       $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

5. Clean Yum repository cache

       $ sudo yum clean expire-cache

6. Install CUDA

       $ sudo yum install cuda

   If the i686 libvdpau package dependency fails to install, try using the following steps to fix the issue:

       $ yumdownloader libvdpau.i686
       $ sudo rpm -U --oldpackage libvdpau*.rpm

7. Add libcuda.so symbolic link, if necessary

   The libcuda.so library is installed in the /usr/lib{,64}/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib{,64} directory.

8. Perform the post-installation actions.

### 3.3. Fedora

1. Perform the pre-installation actions.
2. Address custom xorg.conf, if applicable

   The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf. If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

3. Install repository meta-data

       $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

4. Clean Yum repository cache

       $ sudo yum clean expire-cache

5. Install CUDA

       $ sudo yum install cuda

   If the i686 libvdpau package dependency fails to install, try using the following steps to fix the issue:

       $ yumdownloader libvdpau.i686
       $ sudo rpm -U --oldpackage libvdpau*.rpm

6. Add libcuda.so symbolic link, if necessary

   The libcuda.so library is installed in the /usr/lib{,64}/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib{,64} directory.

7. Perform the post-installation actions.

### 3.4. SLES

1. Perform the pre-installation actions.
2. Install repository meta-data

       $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

3. Refresh Zypper repository cache

       $ sudo zypper refresh

4. Install CUDA

       $ sudo zypper install cuda

   The driver is provided in multiple packages, nvidia-gfxG03-kmp-desktop, nvidia-gfxG03-kmp-default, nvidia-gfxG03-kmp-trace, and their Unified Memory variants. When installing cuda, the correct driver packages should also be specified. Without doing this, zypper will select packages that may not work on the system. Run the following to detect the flavor of kernel and install cuda with the appropriate driver packages:

       $ uname -r
         3.4.6-2.10-<flavor>
       $ sudo zypper install cuda nvidia-gfxG03-kmp-<flavor> \
                             nvidia-uvm-gfxG03-kmp-<flavor>

5. Add the user to the video group

       $ sudo usermod -a -G video <username>

6. Install CUDA Samples GL dependencies

   The CUDA Samples package on SLES does not include dependencies on GL and X11 libraries as these are provided in the SLES SDK. These packages must be installed separately, depending on which samples you want to use.

7. Perform the post-installation actions.

### 3.5. OpenSUSE

1. Perform the pre-installation actions.
2. Install repository meta-data

       $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

3. Refresh Zypper repository cache

       $ sudo zypper refresh

4. Install CUDA

       $ sudo zypper install cuda

   The driver is provided in multiple packages, nvidia-gfxG03-kmp-desktop, nvidia-gfxG03-kmp-default, nvidia-gfxG03-kmp-trace, and their Unified Memory variants. When installing cuda, the correct driver packages should also be specified. Without doing this, zypper will select packages that may not work on the system. Run the following to detect the flavor of kernel and install cuda with the appropriate driver packages:

       $ uname -r
         3.4.6-2.10-<flavor>
       $ sudo zypper install cuda nvidia-gfxG03-kmp-<flavor> \
                             nvidia-uvm-gfxG03-kmp-<flavor>

5. Add the user to the video group

       $ sudo usermod -a -G video <username>

6. Perform the post-installation actions.

### 3.6. Ubuntu

1. Perform the pre-installation actions.
2. Enable armhf foreign architecture, if necessary

   The armhf foreign architecture must be enabled in order to install the cross-armhf toolkit. To enable armhf as a foreign architecture, the following commands must be executed:

   On Ubuntu 12.04,

       $ sudo sh -c \
         'echo "foreign-architecture armhf" >> /etc/dpkg/dpkg.cfg.d/multiarch'
       $ sudo apt-get update

   On Ubuntu 12.10 or greater,

       $ sudo dpkg --add-architecture armhf
       $ sudo apt-get update

3. Install repository meta-data

   When using a proxy server with aptitude, ensure that `wget` is set up to use the same proxy settings before installing the cuda-repo package.

       $ sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

4. Update the Apt repository cache

       $ sudo apt-get update

5. Install CUDA

       $ sudo apt-get install cuda

6. Add libcuda.so symbolic link, if necessary

   The libcuda.so library is installed in the /usr/lib/nvidia-331 directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib directory.

7. Perform the post-installation actions.

### 3.7. L4T

1. Perform the pre-installation actions.
2. Install repository meta-data

       $ sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

3. Update the Apt repository cache

       $ sudo apt-get update

4. Install CUDA Toolkit

       $ sudo apt-get install cuda-toolkit-6-0

5. Add the user to the video group

       $ sudo usermod -a -G video <username>

6. Perform the post-installation actions.

### 3.8. Additional Package Manager Capabilities

Below are some additional capabilities of the package manager that users can take advantage of.

### 3.8.1. Available Packages

The recommended installation packages are `cuda` and `cuda-cross`. Those two packages will install the full set of other CUDA packages required for development and should cover most scenarios

The `cuda` package installs all the available packages for native developments. That includes the compiler, the debugger, the profiler, the math libraries,... For x86 patforms, this also include NSight Eclipse Edition and the visual profiler It also includes the NVIDIA driver package.

The `cuda-cross` package installs all the packages required for cross-platform developments, such as the i386 and x86_64 CUDA libraries. On supported platforms, the `cuda-cross-armhf` package installs all the packages required for cross-platform development on ARMv7. The libraries and header files of the ARMv7 display driver package are also installed to enable the cross compilation of ARMv7 applications. The `cuda-cross` and `cuda-cross-armhf` packages do not install the native display driver.

The packages installed by the packages above can also be installed individually by specifying their names explicitly. The list of available packages be can obtained with:

    $ yum --disablerepo="*" --enablerepo="cuda*" list available    # RedHat & Fedora
    $ zypper packages -r cuda                                      # OpenSUSE & SLES
    $ cat /var/lib/apt/lists/*cuda*Packages | grep "Package:"      # Ubuntu

### 3.8.2. Package Upgrades

The `cuda` package points to the latest stable release of the CUDA Toolkit. When a new version is available, use the following commands to upgrade the toolkit:

    $ sudo yum install cuda                                        # RedHat & Fedora
    $ sudo zypper install cuda                                     # OpenSUSE & SLES
    $ sudo apt-get install cuda                                    # Ubuntu

The `cuda-cross` and `cuda-cross-armhf` packages can also be upgraded in the same manner.

The cuda-drivers package points to the latest driver release available in the CUDA repository. When a new version is available, use the following commands to upgrade the driver:

    $ sudo yum install cuda-drivers                                # RedHat & Fedora
    $ sudo zypper install cuda-drivers \
                          nvidia-gfxG03-kmp-<flavor> \
                          nvidia-uvm-gfxG03-kmp-<flavor>           # OpenSUSE & SLES
    $ sudo apt-get install cuda-drivers                            # Ubuntu

Some desktop environments, such as GNOME or KDE, will display an notification alert when new packages are available.

To avoid any automatic upgrade, and lock down the toolkit installation to the X.Y release, install the `cuda-X-Y` or `cuda-cross-X-Y`.

Side-by-side installations are supported. For instance, to install both the X.Y CUDA Toolkit and the X.Y+1 CUDA Toolkit, install the `cuda-X.Y` and `cuda-X.Y+1` packages.

## 4. Runfile Installation

This section describes the installation and configuration of CUDA when using the standalone installer.

### 4.1. Pre-installation Setup

Before the stand-alone installation can be run, perform the pre-installation actions.

### 4.2. Prerequisites

If you have already installed a standalone CUDA driver and desire to keep using it, you need to make sure it meets the minimum version requirement for the toolkit. This requirement can be found in the CUDA Toolkit release notes. With many distributions, the driver version number can be found in the graphical interface menus under `Applications > System Tools > NVIDIA X Server Settings.`. On the command line, the driver version number can be found by running `/usr/bin/nvidia-settings`.

The package manager installations (RPM/DEB packages) and the stand-alone installer installations (.run file) are incompatible. See below about how to uninstall any previous RPM/DEB installation.

### 4.3. Contents

The standalone installer can install any combination of the NVIDIA Driver (that includes the CUDA Driver), the CUDA Toolkit, or the CUDA Samples. If needed, each individual installer can be extracted by using the `-extract=/absolute/path/to/extract/location/`. The extraction path must be an absolute path.

The CUDA Toolkit installation includes a read-only copy of the CUDA Samples. The read-only copy is used to create a writable copy of the CUDA Samples at some other location at any point in time. To create this writable copy, use the `cuda-install-samples-6.0.sh` script provided with the toolkit. It is equivalent to installing the CUDA Samples with the standalone installer.

### 4.4. Graphical Interface Shutdown

Exit the GUI if you are in a GUI environment by pressing Ctrl-Alt-Backspace. Some distributions require you to press this sequence twice in a row; others have disabled it altogether in favor of a command such as `sudo service ligthdm stop`. Still others require changing the system runlevel using a command such as `/sbin/init 3` Consult your distribution's documentation to find out how to properly exit the GUI. This step is only required in the event that you want to install the NVIDIA Display Driver included in the standalone installer.

### 4.5. NVIDIA Driver RPM/Deb package uninstallation

If you want to install the NVIDIA Display Driver included in the standalone installer, any previous driver installed through RPM or DEB packages MUST be uninstalled first. Such installation may be part of the default installation of your Linux distribution. Or it could have been installed as part of the package installation described in the previous section. To uninstall a DEB package, use `sudo apt-get --purge remove package_name` or equivalent. To uninstall a RPM package, use `sudo yum remove package_name` or equivalent.

### 4.6. Installation

To install any combination of the driver, toolkit, and the samples, simply execute the .run script. The installation of the driver requires the script to be run with root privileges. Depending on the target location, the toolkit and samples installations may also require root privileges.

By default, the toolkit and the samples will install under `/usr/local/cuda-6.0` and `$(HOME)/NVIDIA_CUDA-6.0_Samples`, respectively. In addition, a symbolic link is created from `/usr/local/cuda` to `/usr/local/cuda-6.0`. The symbolic link is created in order for existing projects to automatically make use of the newly installed CUDA Toolkit.

If the target system includes both an integrated GPU (iGPU) and a discrete GPU (dGPU), the --no-opengl-libs option must be used. Otherwise, the openGL library used by the graphics driver of the iGPU will be overwritten and the GUI will not work. In addition, the xorg.conf update at the end of the installation must be declined.

Note: Installing Mesa may overwrite the `/usr/lib/libGL.`so that was previously installed by the NVIDIA driver, so a reinstallation of the NVIDIA driver might be required after installing these libraries.

### 4.7. Interaction with Nouveau

The Nouveau drivers may be installed into your root filesystem (initramfs) and may cause the Display Driver installation to fail. To fix the situation, the initramfs image must be rebuilt with:

    sudo mv /boot/initramfs-$(uname -r).img /boot/initramfs-$(uname -r)-nouveau.img
    sudo dracut /boot/initramfs-$(uname -r).img $(uname -r)

if Grub2 is used as the bootloader, the `rdblacklist=nouveau nouveau.modeset=0` line must be added at the end of the GRUB_CMDLINE_LINUX entry in /etc/default/grub. Then, the Grub configuration must be remade by running:

    sudo grub2-mkconfig -o /boot/grub2/grub.cfg

Once this is done, the machine must be rebooted and the installation attempted again.

### 4.8. Extra Libraries

If you wish to build all the samples, including those with graphical rather than command-line interfaces, additional system libraries or headers may be required. While every Linux distribution is slightly different with respect to package names and package installation procedures, the libraries and headers most likely to be necessary are OpenGL (e.g., Mesa), GLU, GLUT, and X11 (including Xi, Xmu, and GLX).

On Ubuntu, those can be installed as follows:

    sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

### 4.9. Verifications

Check that the device files`/dev/nvidia*` exist and have the correct (0666) file permissions. These files are used by the CUDA Driver to communicate with the kernel-mode portion of the NVIDIA Driver. Applications that use the NVIDIA driver, such as a CUDA application or the X server (if any), will normally automatically create these files if they are missing using the setuid `nvidia-modprobe` tool that is bundled with the NVIDIA Driver. Some systems disallow setuid binaries, however, so if these files do not exist, you can create them manually either by running the command `nvidia-smi` as root at boot time or by using a startup script such as the one below:

    #!/bin/bash

    /sbin/modprobe nvidia

    if [ "$?" -eq 0 ]; then
      # Count the number of NVIDIA controllers found.
      NVDEVS=`lspci | grep -i NVIDIA`
      N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
      NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`

      N=`expr $N3D + $NVGA - 1`
      for i in `seq 0 $N`; do
        mknod -m 666 /dev/nvidia$i c 195 $i
      done

      mknod -m 666 /dev/nvidiactl c 195 255

    else
      exit 1
    fi

    /sbin/modprobe nvidia-uvm

    if [ "$?" -eq 0 ]; then
      # Find out the major device number used by the nvidia-uvm driver
      D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`

      mknod -m 666 /dev/nvidia-uvm c $D 0
    else
      exit 1
    fi

### 4.10. Graphical Interface Restart

Restart the GUI environment using the command `startx`, `init 5`, `sudo service lightdm start`, or the equivalent command on your system.

### 4.11. Post-installation Setup

Once the stand-alone installation is complete, be sure to perform the post-installation actions.

## 5. Post-installation Actions

Some actions must be taken after installing the CUDA Toolkit and Driver before they can be completely used:

- Setup evironment variables.
- Install a writable copy of the CUDA Samples.
- Verify the installation.

### 5.1. Environment Setup

The `PATH` variable needs to include `/usr/local/cuda-6.0/bin`

The `LD_LIBRARY_PATH` variable needs to contain `/usr/local/cuda-6.0/lib` on a 32-bit system, and `/usr/local/cuda-6.0/lib64` on a 64-bit system

- To change the environment variables for 32-bit operating systems:

      $ export PATH=/usr/local/cuda-6.0/bin:$PATH
      $ export LD_LIBRARY_PATH=/usr/local/cuda-6.0/lib:$LD_LIBRARY_PATH

- To change the environment variables for 64-bit operating systems:

      $ export PATH=/usr/local/cuda-6.0/bin:$PATH
      $ export LD_LIBRARY_PATH=/usr/local/cuda-6.0/lib64:$LD_LIBRARY_PATH

### 5.2. (Optional) Install Writable Samples

In order to modify, compile, and run the samples, the samples must be installed with write permissions. A convenience installation script is provided:

    $ cuda-install-samples-6.0.sh <dir>

This script is installed with the cuda-samples-6-0 package. The cuda-samples-6-0 package installs only a read-only copy in /usr/local/cuda-6.0/samples.

### 5.3. Verify the Installation

Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.

Note: Ensure the PATH and LD_LIBRARY_PATH variables are set correctly.

### 5.3.1. Verify the Driver Version

If you installed the driver, verify that the correct version of it is installed.

This can be done through your System Properties (or equivalent) or by executing the command

    cat /proc/driver/nvidia/version

Note that this command will not work on an iGPU/dGPU system.

### 5.3.2. Compiling the Examples

The version of the CUDA Toolkit can be checked by running `nvcc -V` in a terminal window. The `nvcc` command runs the compiler driver that compiles CUDA programs. It calls the `gcc` compiler for C code and the NVIDIA PTX compiler for the CUDA code.

The NVIDIA CUDA Toolkit includes sample programs in source form. You should compile them by changing to `~/NVIDIA_CUDA-6.0_Samples` and typing `make`. The resulting binaries will be placed under `~/NVIDIA_CUDA-6.0_Samples/bin`.

### 5.3.3. Running the Binaries

After compilation, find and run `deviceQuery` under `~/NVIDIA_CUDA-6.0_Samples`. If the CUDA software is installed and configured correctly, the output for `deviceQuery` should look similar to that shown in Figure 1.

Figure 1. Valid Results from deviceQuery CUDA Sample

The exact appearance and the output lines might be different on your system. The important outcomes are that a device was found (the first highlighted line), that the device matches the one on your system (the second highlighted line), and that the test passed (the final highlighted line).

If a CUDA-capable device and the CUDA Driver are installed but `deviceQuery` reports that no CUDA-capable devices are present, this likely means that the `/dev/nvidia*` files are missing or have the wrong permissions.

On systems where `SELinux` is enabled, you might need to temporarily disable this security feature to run `deviceQuery`. To do this, type:

    # setenforce 0

from the command line as the superuser.

Running the `bandwidthTest` program ensures that the system and the CUDA-capable device are able to communicate correctly. Its output is shown in Figure 2.

Figure 2. Valid Results from bandwidthTest CUDA Sample

Note that the measurements for your CUDA-capable device description will vary from system to system. The important point is that you obtain measurements, and that the second-to-last line (in Figure 2) confirms that all necessary tests passed.

Should the tests not pass, make sure you have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

If you run into difficulties with the link step (such as libraries not being found), consult the Linux Release Notes found in the `doc` folder in the CUDA Samples directory.

## 6. Additional Considerations

Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C Programming Guide, located in `/usr/local/cuda-6.0/doc`.

A number of helpful development tools are included in the CUDA Toolkit to assist you as you develop your CUDA programs, such as NVIDIA® Nsight™ Eclipse Edition, NVIDIA Visual Profiler, cuda-gdb, and cuda-memcheck.

For technical support on programming questions, consult and participate in the developer forums at http://developer.nvidia.com/cuda/.

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