How to check cuda version windows 10

The installation instructions for the CUDA Toolkit on MS-Windows systems.

1. Introduction

CUDA В® 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).

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

1.1. System Requirements

The next two tables list the currently supported Windows operating systems and compilers.

Table 1. Windows Operating System Support in CUDA 11.3

Operating System	Native x86_64	Cross (x86_32 on x86_64)
Windows 10	YES	YES
Windows Server 2019	YES	NO
Windows Server 2016	YES	NO

Table 2. Windows Compiler Support in CUDA 11.3

Compiler*	IDE	Native x86_64	Cross (x86_32 on x86_64)
MSVC Version 192x	Visual Studio 2019 16.x	YES	NO
MSVC Version 191x	Visual Studio 2017 15.x (RTW and all updates)	YES	NO
MSVC Version 1900	Visual Studio 2015 * 14.0 (RTW and updates 1, 2, and 3)	YES (deprecated)	NO
	Visual Studio Community 2015 *	YES (deprecated)	NO

* Support for Visual Studio 2015 is deprecated in release 11.1.

x86_32 support is limited. See the x86 32-bit Support section for details.

For more information on MSVC versions, Visual Studio product versions, visit https://dev.to/yumetodo/list-of-mscver-and-mscfullver-8nd.

1.2. x86 32-bit Support

Native development using the CUDA Toolkit on x86_32 is unsupported. Deployment and execution of CUDA applications on x86_32 is still supported, but is limited to use with GeForce GPUs. To create 32-bit CUDA applications, use the cross-development capabilities of the CUDA Toolkit on x86_64.

1.3. About This Document

This document is intended for readers familiar with Microsoft Windows operating systems and the Microsoft Visual Studio environment. You do not need previous experience with CUDA or experience with parallel computation.

2. Installing CUDA Development Tools

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

2.1. Verify You Have a CUDA-Capable GPU

You can verify that you have a CUDA-capable GPU through the Display Adapters section in the Windows Device Manager. Here you will find the vendor name and model of your graphics card(s). If you have an NVIDIA card that is listed in http://developer.nvidia.com/cuda-gpus, that GPU is CUDA-capable. The Release Notes for the CUDA Toolkit also contain a list of supported products.

2.2. Download the NVIDIA CUDA Toolkit

The CUDA Toolkit installs 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, follow the instructions at http://support.microsoft.com/kb/889768.

2.3. Install the CUDA Software

Before installing the toolkit, you should read the Release Notes , as they provide details on installation and software functionality.

Graphical Installation

Install the CUDA Software by executing the CUDA installer and following the on-screen prompts.

Silent Installation

The installer can be executed in silent mode by executing the package with the -s flag. Additional parameters can be passed which will install specific subpackages instead of all packages. See the table below for a list of all the subpackage names.

Table 3. Possible Subpackage Names

Source code for many example CUDA applications using supported versions of Visual Studio.

Note: C:\ProgramData\ is a hidden folder. It can be made visible within the Windows Explorer options at (Tools | Options).

Subpackage Name	Subpackage Description
Toolkit Subpackages (defaults to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v 11.3 )
nvcc_ 11.3	CUDA compiler.
cuobjdump_ 11.3	Extracts information from cubin files.
nvprune_ 11.3	Prunes host object files and libraries to only contain device code for the specified targets.
cupti_ 11.3	The CUDA Profiler Tools Interface for creating profiling and tracing tools that target CUDA applications.
memcheck_ 11.3	Functional correctness checking suite.
nvdisasm_ 11.3	Extracts information from standalone cubin files.
nvprof_ 11.3	Tool for collecting and viewing CUDA application profiling data from the command-line.
visual_profiler_ 11.3	GUI profiler for CUDA applications.
visual_studio_integration_ 11.3	Installs CUDA project wizard and builds customization files in VS.
nsight_vse_ 11.3	Installs the Nsight Visual Studio Edition plugin in all VS.
nsight_nvtx_ 11.3	Installs NVTX on Windows.
demo_suite_ 11.3	Prebuilt demo applications using CUDA.
documentation_ 11.3	CUDA HTML and PDF documentation files including the CUDA C++ Programming Guide, CUDA C++ Best Practices Guide, CUDA library documentation, etc.
cublas_ 11.3	CUBLAS runtime libraries.
cublas_dev_ 11.3	CUBLAS development libraries and headers.
cudart_ 11.3	CUDART runtime libraries.
cufft_ 11.3	CUFFT runtime libraries.
cufft_dev_ 11.3	CUFFT development libraries and headers.
curand_ 11.3	CURAND runtime libraries.
curand_dev_ 11.3	CURAND development libraries and headers.
cusolver_ 11.3	CUSOLVER runtime libraries.
cusolver_dev_ 11.3	CUSOLVER development libraries and headers.
cusparse_ 11.3	CUSPARSE runtime libraries.
cusparse_dev_ 11.3	CUSPARSE development libraries and headers.
npp_ 11.3	NPP runtime libraries.
npp_dev_ 11.3	NPP development libraries and headers.
nvrtc_ 11.3	NVRTC runtime libraries.
nvrtc_dev_ 11.3	NVRTC development libraries and headers.
nvml_dev_ 11.3	NVML development libraries and headers.
occupancy_calculator_ 11.3	Excel spreadsheet used to test occupancy scenarios.
Samples Subpackages (defaults to C:\ProgramData\NVIDIA Corporation\CUDA Samples\v 11.3 )
samples_ 11.3
Driver Subpackages
Display.Driver	The NVIDIA Display Driver. Required to run CUDA applications.

Extracting and Inspecting the Files Manually

Sometimes it may be desirable to extract or inspect the installable files directly, such as in enterprise deployment, or to browse the files before installation. The full installation package can be extracted using a decompression tool which supports the LZMA compression method, such as 7-zip or WinZip.

Once extracted, the CUDA Toolkit files will be in the CUDAToolkit folder, and similarily for the CUDA Samples and CUDA Visual Studio Integration. Within each directory is a .dll and .nvi file that can be ignored as they are not part of the installable files.

2.3.1. Uninstalling the CUDA Software

All subpackages can be uninstalled through the Windows Control Panel by using the Programs and Features widget.

2.4. Use a Suitable Driver Model

On Windows 7 and later, the operating system provides two under which the NVIDIA Driver may operate:

• The driver model is used for display devices.
• The mode of the NVIDIA Driver is available for non-display devices such as NVIDIA Tesla GPUs, and the GeForce GTX Titan GPUs; it uses the Windows driver model.

The TCC driver mode provides a number of advantages for CUDA applications on GPUs that support this mode. For example:

• TCC eliminates the timeouts that can occur when running under WDDM due to the Windows Timeout Detection and Recovery mechanism for display devices.
• TCC allows the use of CUDA with Windows Remote Desktop, which is not possible for WDDM devices.
• TCC allows the use of CUDA from within processes running as Windows services, which is not possible for WDDM devices.
• TCC reduces the latency of CUDA kernel launches.

TCC is enabled by default on most recent NVIDIA Tesla GPUs. To check which driver mode is in use and/or to switch driver modes, use the nvidia-smi tool that is included with the NVIDIA Driver installation (see nvidia-smi -h for details).

2.5. 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.

2.5.1. Running the Compiled Examples

The version of the CUDA Toolkit can be checked by running nvcc -V in a Command Prompt window. You can display a Command Prompt window by going to:

Start > All Programs > Accessories > Command Prompt

This assumes that you used the default installation directory structure. If CUDA is installed and configured correctly, the output should look similar to Figure 1.

The exact appearance and the output lines might be different on your system. The important outcomes are that a device was found, that the device(s) match what is installed in your system, and that the test passed.

If a CUDA-capable device and the CUDA Driver are installed but deviceQuery reports that no CUDA-capable devices are present, ensure the deivce and driver are properly installed.

Running the bandwidthTest program, located in the same directory as deviceQuery above, ensures that the system and the CUDA-capable device are able to communicate correctly. The output should resemble Figure 2.

The device name (second line) and the bandwidth numbers vary from system to system. The important items are the second line, which confirms a CUDA device was found, and the second-to-last line, which confirms that all necessary tests passed.

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

3. Compiling CUDA Programs

CUDA Samples are organized according to . Each sample is organized into one of the following folders: ( 0_Simple , 1_Utilities , 2_Graphics , 3_Imaging , 4_Finance , 5_Simulations , 6_Advanced , 7_CUDALibraries ).

3.1. Compiling Sample Projects

The bandwidthTest project is a good sample project to build and run. It is located in the NVIDIA Corporation\CUDA Samples\v 11.3 \1_Utilities\bandwidthTest directory.

If you elected to use the default installation location, the output is placed in CUDA Samples\v 11.3 \bin\win64\Release . Build the program using the appropriate solution file and run the executable. If all works correctly, the output should be similar to Figure 2.

3.2. Sample Projects

The sample projects come in two configurations: debug and release (where release contains no debugging information) and different Visual Studio projects.

A few of the example projects require some additional setup.

These sample projects also make use of the $CUDA_PATH environment variable to locate where the CUDA Toolkit and the associated .props files are.

The environment variable is set automatically using the Build Customization CUDA 11.3 .props file, and is installed automatically as part of the CUDA Toolkit installation process.

Table 4. CUDA Visual Studio .props locations

Visual Studio	CUDA 11.3 .props file Install Directory
Visual Studio 2015 (deprecated)	C:\Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\V140\BuildCustomizations
Visual Studio 2017	\Common7\IDE\VC\VCTargets\BuildCustomizations
Visual Studio 2019	C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\MSBuild\Microsoft\VC\v160\BuildCustomizations

You can reference this CUDA 11.3 .props file when building your own CUDA applications.

3.3. Build Customizations for New Projects

When creating a new CUDA application, the Visual Studio project file must be configured to include CUDA build customizations. To accomplish this, click File-> New | Project. NVIDIA-> CUDA->, then select a template for your CUDA Toolkit version. For example, selecting the «CUDA 11.3 Runtime» template will configure your project for use with the CUDA 11.3 Toolkit. The new project is technically a C++ project (.vcxproj) that is preconfigured to use NVIDIA’s Build Customizations. All standard capabilities of Visual Studio C++ projects will be available.

To specify a custom CUDA Toolkit location, under CUDA C/C++, select Common, and set the CUDA Toolkit Custom Dir field as desired. Note that the selected toolkit must match the version of the Build Customizations.

3.4. Build Customizations for Existing Projects

While Option 2 will allow your project to automatically use any new CUDA Toolkit version you may install in the future, selecting the toolkit version explicitly as in Option 1 is often better in practice, because if there are new CUDA configuration options added to the build customization rules accompanying the newer toolkit, you would not see those new options using Option 2.

Files which contain CUDA code must be marked as a CUDA C/C++ file. This can done when adding the file by right clicking the project you wish to add the file to, selecting Add\New Item , selecting NVIDIA CUDA 11.3 \Code\CUDA C/C++ File , and then selecting the file you wish to add.

4. 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 the CUDA Toolkit documentation directory.

A number of helpful development tools are included in the CUDA Toolkit or are available for download from the NVIDIA Developer Zone to assist you as you develop your CUDA programs, such as NVIDIA В® Nsightв„ў Visual Studio Edition, NVIDIA Visual Profiler, 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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