Windows平台CUDA开发之前的准备工作

转自http://blog.csdn.net/carson2005/article/details/46416859

CUDA是NVIDIA的GPU开发工具，目前在大规模并行计算领域有着广泛应用。

windows平台上面的CUDA开发之前，最好去NVIDIA官网查看说明，然后下载相应的driver， ToolKits等等。如果你下载最新版本的CUDA7.0，里面其实已经包含了driver及Tool kits。

特别要注意：目标最高版本为CUDA7.0，仅支持64位系统（32位没法安装CUDA 7.0 Tool Kits），另外，VS编译平台最低要求是VS2010。 So,那些依然用VC6或者VS2008的就别犹豫了，赶快换平台吧。

下面贴出NVIDIA官网的start guide:(坑爹的NVIDIA，在develop的地方以及下载tool kits的地方，都没有提到这个start guide，造成笔者反复安装配置了多次，奔溃。。。。)

NVIDIA CUDA Getting Started Guide for Microsoft Windows

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:

• A CUDA-capable GPU
• A supported version of Microsoft Windows
• A supported version of Microsoft Visual Studio
• the NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)

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

Table 1. Windows Operating System Support in CUDA 7.0

Operating System	Native x86_64	Cross (x86_32 on x86_64)
Windows 8.1	YES	YES
Windows 7	YES	YES
Windows Server 2012 R2	YES	YES
Windows Server 2008 R2	YES	YES

Table 2. Windows Compiler Support in CUDA 7.0

Compiler	IDE	Native x86_64	Cross (x86_32 on x86_64)
Visual C++ 12.0	Visual Studio 2013	YES	YES
	Visual Studio Community 2013	YES	NO
Visual C++ 11.0	Visual Studio 2012	YES	YES
Visual C++ 10.0	Visual Studio 2010	YES	YES

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

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.

Support for developing and running x86 32-bit applications on x86_64 Windows is limited to use with:

• GeForce GPUs
• CUDA Driver
• CUDA Runtime (cudart)
• CUDA Math Library (math.h)
• CUDA C++ Compiler (nvcc)
• CUDA Development Tools

1.2. 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

The setup of CUDA development tools on a system running the appropriate version of Windows consists of a few simple steps:

• Verify the system has a CUDA-capable GPU.
• Download the NVIDIA CUDA Toolkit.
• Install the NVIDIA CUDA Toolkit.
• Test that the installed software runs correctly and communicates with the hardware.

2.1. Verify You Have a CUDA-Capable GPU

To verify that your GPU is CUDA-capable, open the Control Panel (Start > Control Panel) and double click on System. In the System Properties window that opens, click the Hardware tab, then Device Manager. Expand the Display adapters entry. There you will find the vendor name and model of your graphics card. If it is an NVIDIA card that 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. 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 one of the following installer formats:

1. Network Installer: A minimal installer which later downloads packages required for installation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.
2. Full Installer: An installer which contains all the components of the CUDA Toolkit and does not require any further download. This installer is useful for systems which lack network access and for enterprise deployment.

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.

Note: The driver and toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver from the NVIDIA CUDA Toolkit.

Note: The installation may fail if Windows Update starts after the installation has begun. Wait until Windows Update is complete and then try the installation again.

Graphical Installation

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

Silent Installation

Alternatively, the installer can be executed in silent mode by executing the package with the -s flag. Additional flags can be passed which will install specific subpackages instead of all packages. Allowed subpackage names are: CUDAToolkit_7.0, CUDASamples_7.0, CUDAVisualStudioIntegration_7.0, and Display.Driver. For example, to install only the driver and the toolkit components:

<PackageName>.exe -s CUDAToolkit_<span class="keyword" style="margin-left: 0px; color: rgb(0, 0, 0);">7.0</span> Display.Driver

Subpackage Details

• Display Driver

  Required to run CUDA applications.

• CUDA Toolkit

  The CUDA Toolkit installation defaults to C:Program FilesNVIDIA GPU Computing ToolkitCUDAv7.0. This directory contains the following:

  Bin

  the compiler executables and runtime libraries

  Include

  the header files needed to compile CUDA programs

  Lib

  the library files needed to link CUDA programs

  Doc

  the CUDA documentation, including:

  • CUDA C Programming Guide
  • CUDA C Best Practices Guide
  • documentation for the CUDA libraries
  • other CUDA Toolkit-related documentation

• CUDA Visual Studio Integration

  The CUDA Visual Studio Integration registers the CUDA plugins with the supported and installed versions of Visual Studio on the system and installs Nsight Visual Studio Edition. This integration allows for CUDA development within Visual Studio.

• CUDA Samples

  The CUDA Samples contain source code for many example problems and templates with Microsoft Visual Studio 2010, 2012, and 2013 projects.

  The CUDA Samples installation defaults to C:ProgramDataNVIDIA CorporationCUDA Samplesv7.0.

  Note:C:ProgramData is a hidden folder. It can be made visible within Windows Explorer by enabling it through the menu options within Windows Explorer (Tools | Options).

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.

Note: Accessing the files in this manner does not set up any environment settings, such as variables or Visual Studio integration. This is intended for enterprise-level deployment.

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 driver models under which the NVIDIA Driver may operate:

• The WDDM driver model is used for display devices.
• The Tesla Compute Cluster (TCC) mode of the NVIDIA Driver is available for non-display devices such as NVIDIA Tesla GPUs; it uses the Windows WDM 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).

Note: Keep in mind that when TCC mode is enabled for a particular GPU, that GPU cannot be used as a display device.

Note: NVIDIA GeForce GPUs do not support TCC mode.

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

CUDA Samples include sample programs in both source and compiled form. To verify a correct configuration of the hardware and software, it is highly recommended that you run the deviceQuery program located at

C:ProgramDataNVIDIA CorporationCUDA Samplesv7.0inwin64Release

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.

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, 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.

Figure 2. Valid Results from bandwidthTest CUDA Sample

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.

To see a graphical representation of what CUDA can do, run the sample Particles executable at

C:ProgramDataNVIDIA CorporationCUDA Samplesv7.0inwin64Release

3. Compiling CUDA Programs

The project files in the CUDA Samples have been designed to provide simple, one-click builds of the programs that include all source code. To build the Windows projects (for release or debug mode), use the provided *.sln solution files for Microsoft Visual Studio 2010, 2012, or 2013. You can use either the solution files located in each of the examples directories in

C:ProgramDataNVIDIA CorporationCUDA Samplesv7.0<category><sample_name>

or the global solution files Samples*.sln located in

C:ProgramDataNVIDIA CorporationCUDA Samplesv7.0

CUDA Samples are organized according to <category>. 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 CorporationCUDA Samplesv7.01_UtilitiesandwidthTest directory.

If you elected to use the default installation location, the output is placed in CUDA Samplesv7.0inwin64Release. 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. The simpleD3D9 example requires the system to have a Direct3D SDK (June 2010 or later) installed and the Visual C++ directory paths (located in Tools > Options...) properly configured. Consult the Direct3D documentation for additional details.

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 7.0.props file, and is installed automatically as part of the CUDA Toolkit installation process.

For Visual Studio 2010, the CUDA 7.0.props file is installed into:

C:Program Files (x86)MSBuildMicrosoft.Cppv4.0BuildCustomizations

For Visual Studio 2012, the CUDA 7.0.props file is installed into:

C:Program Files (x86)MSBuildMicrosoft.Cppv4.0V110BuildCustomizations

For Visual Studio 2013, the CUDA 7.0.props file is installed into:

C:Program Files (x86)MSBuildMicrosoft.Cppv4.0V120BuildCustomizations

You can reference this CUDA 7.0.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 7.0 Runtime" template will configure your project for use with the CUDA 7.0 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

When adding CUDA acceleration to existing applications, the relevant Visual Studio project files must be updated to include CUDA build customizations. This can be done using one of the following two methods:

1. Open the Visual Studio project, right click on the project name, and select Build Customizations..., then select the CUDA Toolkit version you would like to target.
2. Alternatively, you can configure your project always to build with the most recently installed version of the CUDA Toolkit. First add a CUDA build customization to your project as above. Then, right click on the project name and select Properties. Under CUDA C/C++, select Common, and set the CUDA Toolkit Custom Dir field to $(CUDA_PATH) . Note that the $(CUDA_PATH) environment variable is set by the installer.

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.

If you use the $(CUDA_PATH) environment variable to target a version of the CUDA Toolkit for building, and you perform an installation or uninstallation of any version of the CUDA Toolkit, you should validate that the $(CUDA_PATH) environment variable points to the correct installation directory of the CUDA Toolkit for your purposes. You can access the value of the $(CUDA_PATH) environment variable via the following steps:

1. Open a run window from the Start Menu
2. Run:

   control sysdm.cpl

3. Select the "Advanced" tab at the top of the window
4. Click "Environment Variables" at the bottom of the window

Note for advanced users: If you wish to try building your project against a newer CUDA Toolkit without making changes to any of your project files, go to the Visual Studio command prompt, change the current directory to the location of your project, and execute a command such as the following:

msbuild <projectname.extension> /t:Rebuild /p:CudaToolkitDir="drive:/path/to/new/toolkit/"

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^® Nsight™ 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/.

以上英文内容转自：
http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-microsoft-windows/index.html#axzz3W8BU10Ol