CUDA Toolkit 3.2 Downloads

Individual code samples from the SDK are also available.

New and Improved CUDA Libraries

• CUBLAS performance improved 50% to 300% on Fermi architecture GPUs, for matrix multiplication of all datatypes and transpose variations
• CUFFT performance tuned for radix-3, -5, and -7 transform sizes on Fermi architecture GPUs, now 2x to 10x faster than MKL
• New CUSPARSE library of GPU-accelerated sparse matrix routines for sparse/sparse and dense/sparse operations delivers 5x to 30x faster performance than MKL
• New CURAND library of GPU-accelerated random number generation (RNG) routines, supporting Sobol quasi-random and XORWOW pseudo-random routines at 10x to 20x faster than similar routines in MKL
• H.264 encode/decode libraries now included in the CUDA Toolkit

CUDA Driver & CUDA C Runtime

• Support for new 6GB Quadro and Tesla products
• New support for enabling high performance Tesla Compute Cluster (TCC) mode on Tesla GPUs in Windows desktop workstations

Development Tools

• Multi-GPU debugging support for both cuda-gdb and Parallel Nsight
• Expanded cuda-memcheck support for all Fermi architecture GPUs
• NVCC support for Intel C Compiler (ICC) v11.1 on 64-bit Linux distros
• Support for debugging GPUs with more than 4GB device memory

Miscellaneous

• Support for memory management using malloc() and free() in CUDA C compute kernels
• New NVIDIA System Management Interface (nvidia-smi) support for reporting % GPU busy, and several GPU performance counters

New GPU Computing SDK Code Samples

• Several code samples demonstrating how to use the new CURAND library, including MonteCarloCURAND, EstimatePiInlineP, EstimatePiInlineQ, EstimatePiP, EstimatePiQ, SingleAsianOptionP, and randomFog
• Conjugate Gradient Solver, demonstrating the use of CUBLAS and CUSPARSE in the same application
• Function Pointers, a sample that shows how to use function pointers to implement the Sobel Edge Detection filter for 8-bit monochrome images
• Interval Computing, demonstrating the use of interval arithmetic operators using C++ templates and recursion
• Simple Printf, demonstrating best practices for using both printf and cuprintf in compute kernels
• Bilateral Filter, an edge-preserving non-linear smoothing filter for image recovery and denoising implemented in CUDA C with OpenGL rendering
• SLI with Direct3D Texture, a simple example demonstrating the use of SLI and Direct3D interoperability with CUDA C
• cudaEncode, showing how to use the NVIDIA H.264 Encoding Library using YUV frames as input
• Vflocking Direct3D/CUDA, which simulates and visualizes the flocking behavior of birds in flight
• simpleSurfaceWrite, demonstrating how CUDA kernels can write to 2D surfaces on Fermi GPUs

Windows developers should be sure to check out the new debugging and profiling features in Parallel Nsight v1.5 for Visual Studio at www.nvidia.com/ParallelNsight.

Please refer to the Release Notes and Getting Started Guides for more information.

In CUDA Toolkit 3.2 and the accompanying release of the CUDA driver, some important changes have been made to the CUDA Driver API to support large memory access for device code and to enable further system calls such as malloc and free. Please refer to the CUDA Toolkit 3.2 Readiness Tech Brief for a summary of these changes.

Note: The developer driver packages below provide baseline support for the widest number of NVIDIA products in the smallest number of installers. More recent production driver packages for developers and end users may be available at www.nvidia.com/drivers.

For additional tools and solutions for Windows, Linux and MAC OS , such as CUDA Fortran, CULA, CUDA-GDB, please visit our Tools and Ecosystem Page

Cuda toolkit install windows

Minimal first-steps instructions to get CUDA running on a standard system.

1. Introduction

This guide covers the basic instructions needed to install CUDA and verify that a CUDA application can run on each supported platform.

These instructions are intended to be used on a clean installation of a supported platform. For questions which are not answered in this document, please refer to the Windows Installation Guide and Linux Installation Guide.

The CUDA installation packages can be found on the CUDA Downloads Page.

2. Windows

When installing CUDA on Windows, you can choose between the Network Installer and the Local Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Windows Installation Guide.

2.1. Network Installer

Perform the following steps to install CUDA and verify the installation.

1. Launch the downloaded installer package.
2. Read and accept the EULA.
3. Select «next» to download and install all components.
4. Once the download completes, the installation will begin automatically.
5. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
6. Click «close» to close the installer.
7. Navigate to the CUDA Samples’ nbody directory.
8. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

2.2. Local Installer

Perform the following steps to install CUDA and verify the installation.

1. Launch the downloaded installer package.
2. Read and accept the EULA.
3. Select «next» to install all components.
4. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
5. Click «close» to close the installer.
6. Navigate to the CUDA Samples’ nbody directory.
7. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

3. Linux

CUDA on Linux can be installed using an RPM, Debian, or Runfile package, depending on the platform being installed on.

3.1. Linux x86_64

For development on the x86_64 architecture. In some cases, x86_64 systems may act as host platforms targeting other architectures. See the Linux Installation Guide for more details.

3.1.1. Redhat / CentOS

When installing CUDA on Redhat or CentOS, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.1.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
2. Enable optional repos:

On RHEL 7 Linux only, execute the following steps to enable optional repositories.

• On x86_64 workstation:
• On POWER9 system:
• On x86_64 server:

Install the repository meta-data, clean the yum cache, and install CUDA:

Reboot the system to load the NVIDIA drivers.

Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:

Install a writable copy of the samples then build and run the nbody sample:

3.1.1.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.2. Fedora

When installing CUDA on Fedora, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.2.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the RPMFusion free repository to satisfy the Akmods dependency:
2. Install the repository meta-data, clean the dnf cache, and install CUDA:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the nbody sample:

3.1.2.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.3. SUSE Linux Enterprise Server

When installing CUDA on SUSE Linux Enterprise Server, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.3.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
2. Add the user to the video group:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the vectorAdd sample:

3.1.3.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

1. Reboot into runlevel 3 by temporarily adding the number «3» and the word «nomodeset» to the end of the system’s kernel boot parameters.
2. Run the installer silently to install with the default selections (implies acceptance of the EULA):
3. Create an xorg.conf file to use the NVIDIA GPU for display:
4. Reboot the system to load the graphical interface.
5. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
6. Install a writable copy of the samples then build and run the vectorAdd sample:

3.1.4. OpenSUSE

When installing CUDA on OpenSUSE, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.4.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
2. Add the user to the video group:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the nbody sample:

3.1.4.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.5. WSL

These instructions must be used if you are installing in a WSL environment. Do not use the Ubuntu instructions in this case.

Install repository meta-data

When installing using the local repo:

When installing using the network repo:

Pin file to prioritize CUDA repository:

Update the Apt repository cache and install CUDA

3.1.6. Ubuntu

When installing CUDA on Ubuntu, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the Debian installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.6.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the nbody sample:

3.1.6.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.1.7. Debian

When installing CUDA on Debian 10, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Linux Installation Guide.

3.1.7.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the nbody sample:

3.1.7.2. Runfile Installer

Perform the following steps to install CUDA and verify the installation.

3.2. Linux POWER8

For development on the POWER8 architecture.

3.2.1. Ubuntu

When installing CUDA on Ubuntu on POWER8, you must use the Debian Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.2.1.1. Debian Installer

Perform the following steps to install CUDA and verify the installation.

1. Install the repository meta-data, update the apt-get cache, and install CUDA:
2. Reboot the system to load the NVIDIA drivers.
3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
4. Install a writable copy of the samples then build and run the vectorAdd sample:

3.2.2. Redhat / CentOS

When installing CUDA on Redhat on POWER8, you must use the RPM Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.2.2.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
2. Install the repository meta-data, clean the yum cache, and install CUDA:
3. Reboot the system to load the NVIDIA drivers.
4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
5. Install a writable copy of the samples then build and run the vectorAdd sample:

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