1. Release Notes

This section describes the release notes for the CUDA Samples only. For the release notes for the whole CUDA Toolkit, please see CUDA Toolkit Release Notes.

1.1. CUDA 7.0

  • Removed support for Windows 32-bit builds.
  • The Makefile x86_64=1 and ARMv7=1 options have been deprecated. Please use TARGET_ARCH to set the targeted build architecture instead.
  • The Makefile GCC option has been deprecated. Please use HOST_COMPILER to set the host compiler instead.
  • The CUDA Samples are no longer shipped as prebuilt binaries on Windows. Please use VS Solution files provided to build respective executable.
  • Added 0_Simple/clock_nvrtc. Demonstrates how to compile clock function kernel at runtime using libNVRTC to measure the performance of kernel accurately.
  • Added 0_Simple/inlinePTX_nvrtc. Demonstrates compilation of CUDA kernel having PTX embedded at runtime using libNVRTC.
  • Added 0_Simple/matrixMul_nvrtc. Demonstrates compilation of matrix multiplication CUDA kernel at runtime using libNVRTC.
  • Added 0_Simple/simpleAssert_nvrtc. Demonstrates compilation of CUDA kernel having assert() at runtime using libNVRTC.
  • Added 0_Simple/simpleAtomicIntrinsics_nvrtc. Demonstrates compilation of CUDA kernel performing atomic operations at runtime using libNVRTC.
  • Added 0_Simple/simpleTemplates_nvrtc. Demonstrates compilation of templatized dynamically allocated shared memory arrays CUDA kernel at runtime using libNVRTC.
  • Added 0_Simple/simpleVoteIntrinsics_nvrtc. Demonstrates compilation of CUDA kernel which uses vote intrinsics at runtime using libNVRTC.
  • Added 0_Simple/vectorAdd_nvrtc. Demonstrates compilation of CUDA kernel performing vector addition at runtime using libNVRTC.
  • Added 4_Finance/binomialOptions_nvrtc. Demonstrates runtime compilation using libNVRTC of CUDA kernel which evaluates fair call price for a given set of European options under binomial model.
  • Added 4_Finance/BlackScholes_nvrtc. Demonstrates runtime compilation using libNVRTC of CUDA kernel which evaluates fair call and put prices for a given set of European options by Black-Scholes formula.
  • Added 4_Finance/quasirandomGenerator_nvrtc. Demonstrates runtime compilation using libNVRTC of CUDA kernel which implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions.

1.2. CUDA 6.5

  • Added 7_CUDALibraries/cuHook. Demonstrates how to build and use an intercept library with CUDA.
  • Added 7_CUDALibraries/simpleCUFFT_callback. Demonstrates how to compute a 1D-convolution of a signal with a filter using a user-supplied CUFFT callback routine, rather than a separate kernel call.
  • Added 7_CUDALibraries/simpleCUFFT_MGPU. Demonstrates how to compute a 1D-convolution of a signal with a filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPUs.
  • Added 7_CUDALibraries/simpleCUFFT_2d_MGPU. Demonstrates how to compute a 2D-convolution of a signal with a filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPUs.
  • Removed 3_Imaging/cudaEncode. Support for the CUDA Video Encoder (NVCUVENC) has been removed.
  • Removed 4_Finance/ExcelCUDA2007. The topic will be covered in a blog post at Parallel Forall.
  • Removed 4_Finance/ExcelCUDA2010. The topic will be covered in a blog post at Parallel Forall.
  • The 4_Finance/binomialOptions sample is now restricted to running on GPUs with SM architecture 2.0 or greater.
  • The 4_Finance/quasirandomGenerator sample is now restricted to running on GPUs with SM architecture 2.0 or greater.
  • The 7_CUDALibraries/boxFilterNPP sample now demonstrates how to use the static NPP libraries on Linux and Mac.
  • The 7_CUDALibraries/conjugateGradient sample now demonstrates how to use the static CUBLAS and CUSPARSE libraries on Linux and Mac.
  • The 7_CUDALibraries/MersenneTwisterGP11213 sample now demonstrates how to use the static CURAND library on Linux and Mac.

1.3. CUDA 6.0

  • New featured samples that support a new CUDA 6.0 feature called UVM-Lite
  • Added 0_Simple/UnifiedMemoryStreams - new CUDA sample that demonstrates the use of OpenMP and CUDA streams with Unified Memory on a single GPU.
  • Added 1_Utilities/p2pBandwidthTestLatency - new CUDA sample that demonstrates how measure latency between pairs of GPUs with P2P enabled and P2P disabled.
  • Added 6_Advanced/StreamPriorities - This sample demonstrates basic use of the new CUDA 6.0 feature stream priorities.
  • Added 7_CUDALibraries/ConjugateGradientUM - This sample implements a conjugate gradient solver on GPU using cuBLAS and cuSPARSE library, using Unified Memory.

1.4. CUDA 5.5

  • Linux makefiles have been updated to generate code for the AMRv7 architecture. Only the ARM hard-float floating point ABI is supported. Both native ARMv7 compilation and cross compilation from x86 is supported
  • Performance improvements in CUDA toolkit for Kepler GPUs (SM 3.0 and SM 3.5)
  • Makefiles projects have been updated to properly find search default paths for OpenGL, CUDA, MPI, and OpenMP libraries for all OS Platforms (Mac, Linux x86, Linux ARM).
  • Linux and Mac project Makefiles now invoke NVCC for building and linking projects.
  • Added 0_Simple/cppOverload - new CUDA sample that demonstrates how to use C++ overloading with CUDA.
  • Added 6_Advanced/cdpBezierTessellation - new CUDA sample that demonstrates an advanced method of implementing Bezier Line Tessellation using CUDA Dynamic Parallelism. Requires compute capability 3.5 or higher.
  • Added 7_CUDALibrariess/jpegNPP - new CUDA sample that demonstrates how to use NPP for JPEG compression on the GPU.
  • CUDA Samples now have better integration with Nsight Eclipse IDE.
  • 6_Advanced/ptxjit sample now includes a new API to demonstrate PTX linking at the driver level.

1.5. CUDA 5.0

  • New directory structure for CUDA samples. Samples are classified accordingly to categories: 0_Simple, 1_Utilities, 2_Graphics, 3_Imaging, 4_Finance, 5_Simulations, 6_Advanced, and 7_CUDALibraries
  • Added 0_Simple/simpleIPC - CUDA Runtime API sample is a very basic sample that demonstrates Inter Process Communication with one process per GPU for computation. Requires Compute Capability 2.0 or higher and a Linux Operating System.
  • Added 0_Simple/simpleSeparateCompilation - demonstrates a CUDA 5.0 feature, the ability to create a GPU device static library and use it within another CUDA kernel. This example demonstrates how to pass in a GPU device function (from the GPU device static library) as a function pointer to be called. Requires Compute Capability 2.0 or higher.
  • Added 2_Graphics/bindlessTexture - demonstrates use of cudaSurfaceObject, cudaTextureObject, and MipMap support in CUDA. Requires Compute Capability 3.0 or higher.
  • Added 3_Imaging/stereoDisparity - demonstrates how to compute a stereo disparity map using SIMD SAD (Sum of Absolute Difference) intrinsics. Requires Compute Capability 2.0 or higher.
  • Added 0_Simple/cdpSimpleQuicksort - demonstrates a simple quicksort implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.
  • Added 0_Simple/cdpSimplePrint - demonstrates simple printf implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.
  • Added 6_Advanced/cdpLUDecomposition - demonstrates LU Decomposition implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.
  • Added 6_Advanced/cdpAdvancedQuicksort - demonstrates an advanced quicksort implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.
  • Added 6_Advanced/cdpQuadtree - demonstrates Quad Trees implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.
  • Added 7_CUDALibraries/simpleDevLibCUBLAS - implements a simple cuBLAS function calls that call GPU device API library running cuBLAS functions. cuBLAS device code functions take advantage of CUDA Dynamic Parallelism and requires compute capability of 3.5 or higher.

1.6. CUDA 4.2

  • Added segmentationTreeThrust - demonstrates a method to build image segmentation trees using Thrust. This algorithm is based on Boruvka's MST algorithm.

1.7. CUDA 4.1

  • Added MersenneTwisterGP11213 - implements Mersenne Twister GP11213, a pseudorandom number generator using the cuRAND library.
  • Added HSOpticalFlow - When working with image sequences or video it's often useful to have information about objects movement. Optical flow describes apparent motion of objects in image sequence. This sample is a Horn-Schunck method for optical flow written using CUDA.
  • Added volumeFiltering - demonstrates basic volume rendering and filtering using 3D textures.
  • Added simpleCubeMapTexture - demonstrates how to use texcubemap fetch instruction in a CUDA C program.
  • Added simpleAssert - demonstrates how to use GPU assert in a CUDA C program.
  • Added grabcutNPP - CUDA implementation of Rother et al. GrabCut approach using the 8 neighborhood NPP Graphcut primitive introduced in CUDA 4.1. (C. Rother, V. Kolmogorov, A. Blake. GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts. ACM Transactions on Graphics (SIGGRAPH'04), 2004).

2. Getting Started

The CUDA Samples are an educational resource provided to teach CUDA programming concepts. The CUDA Samples are not meant to be used for performance measurements.

For system requirements and installation instructions, please refer to the Linux Getting Started Guide, the Windows Getting Started Guide, and the Mac Getting Started Guide.

2.1. Getting CUDA Samples

Windows

On Windows, the CUDA Samples are installed using the CUDA Toolkit Windows Installer. By default, the CUDA Samples are installed in:
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\
The installation location can be changed at installation time.

Linux

On Linux, to install the CUDA Samples, the CUDA toolkit must first be installed. See the Getting Started Guide for more information on how to install the CUDA Toolkit.

Then the CUDA Samples can be installed by running the following command, where <target_path> is the location where to install the samples:
$ cuda-install-samples-7.0.sh <target_path>

Mac OSX

On Mac OSX, to install the CUDA Samples, the CUDA toolkit must first be installed. See the Getting Started Guide for more information on how to install the CUDA Toolkit.

Then the CUDA Samples can be installed by running the following command, where <target_path> is the location where to install the samples:
$ cuda-install-samples-7.0.sh <target_path>

2.2. Building Samples

Windows

The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
*_vs<version>.sln - for Visual Studio <version>
Complete samples solution files exist at:
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\
Each individual sample has its own set of solution files at:
C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\<sample_dir>\
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
Note: Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (Tools > Options...). Check DirectX Dependencies section for details.

Linux

The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
$ cd <sample_dir>
$ make
The samples makefiles can take advantage of certain options:
  • TARGET_ARCH=<arch> - cross-compile targeting a specific architecture. Allowed architectures are x86_64, armv7l, aarch64, and ppc64le.

    By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equvalent of setting TARGET_ARCH=x86_64.

    $ make TARGET_ARCH=x86_64
    $ make TARGET_ARCH=armv7l
    $ make TARGET_ARCH=aarch64
    $ make TARGET_ARCH=ppc64le
    See here for more details.
  • dbg=1 - build with debug symbols
    $ make dbg=1
  • SMS="A B ..." - override the SM architectures for which the sample will be built, where "A B ..." is a space-delimited list of SM architectures. For example, to generate SASS for SM 20 and SM 30, use SMS="20 30".
    $ make SMS="20 30"
  • HOST_COMPILER=<host_compiler> - override the default g++ host compiler. See the Linux Getting Started Guide for a list of supported host compilers.
    $ make HOST_COMPILER=g++

Mac

The Mac samples are built using makefiles. To use the makefiles, change directory into the sample directory you wish to build, and run make:
$ cd <sample_dir>
$ make
The samples makefiles can take advantage of certain options:
  • dbg=1 - build with debug symbols
    $ make dbg=1
  • SMS="A B ..." - override the SM architectures for which the sample will be built, where "A B ..." is a space-delimited list of SM architectures. For example, to generate SASS for SM 20 and SM 30, use SMS="20 30".
    $ make SMS="A B ..."
  • HOST_COMPILER=<host_compiler> - override the default clang host compiler. See the Mac Getting Started Guide for a list of supported host compilers.
    $ make HOST_COMPILER=clang

2.3. CUDA Cross-Platform Samples

This section describes the options used to build cross-platform samples. TARGET_ARCH=<arch> and TARGET_OS=<os> should be chosen based on the supported targets shown below. TARGET_FS=<path> can be used to point nvcc to libraries and headers used by the sample.

Table 1. Supported Target Arch/OS Combinations
  TARGET OS
linux darwin android qnx
TARGET ARCH x86_64 YES YES NO NO
armv7l YES NO YES YES
aarch64 NO NO YES NO
ppc64le YES NO NO NO

TARGET_ARCH

The target architecture must be specified when cross-compiling applications. If not specified, it defaults to the host architecture. Allowed architectures are:
  • x86_64 - 64-bit x86 CPU architecture
  • armv7l - 32-bit ARM CPU architecture, like that found on Jetson TK1
  • aarch64 - 64-bit ARM CPU architecture, found on certain Android systems
  • ppc64le - 64-bit little-endian IBM POWER8 architecture

TARGET_OS

The target OS must be specified when cross-compiling applications. If not specified, it defaults to the host OS. Allowed OSes are:
  • linux - for any Linux distributions
  • darwin - for Mac OS X
  • android - for any supported device running Android
  • qnx - for any supported device running QNX

TARGET_FS

The most reliable method to cross-compile the CUDA Samples is to use the TARGET_FS variable. To do so, mount the target's filesystem on the host, say at /mnt/target. This is typically done using exportfs. In cases where exportfs is unavailable, it is sufficient to copy the target's filesystem to /mnt/target. To cross-compile a sample, execute:
$ make TARGET_ARCH=<arch> TARGET_OS=<os> TARGET_FS=/mnt/target

Copying Libraries

If the TARGET_FS option is not available, the libraries used should be copied from the target system to the host system, say at /opt/target/libs. If the sample uses GL, the GL headers must also be copied, say at /opt/target/include. The linker must then be told where the libraries are with the -rpath-link and/or -L options. To ignore unresolved symbols from some libraries, use the --unresolved-symbols option as shown below. SAMPLE_ENABLED should be used to force the sample to build. For example, to cross-compile a sample which uses such libraries, execute:
$ make TARGET_ARCH=<arch> TARGET_OS=<os> \
           EXTRA_LDFLAGS="-rpath-link=/opt/target/libs -L/opt/target/libs --unresolved-symbols=ignore-in-shared-libs" \
           EXTRA_CCFLAGS="-I /opt/target/include" \
           SAMPLE_ENABLED=1

2.4. Using CUDA Samples to Create Your Own CUDA Projects

2.4.1. Creating CUDA Projects for Windows

Creating a new CUDA Program using the CUDA Samples infrastructure is easy. We have provided a template and template_runtime project that you can copy and modify to suit your needs. Just follow these steps:

(<category> refers to one of the following folders: 0_Simple, 1_Utilities, 2_Graphics, 3_Imaging, 4_Finance, 5_Simulations, 6_Advanced, 7_CUDALibraries.)

  1. Copy the content of:
    C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\<category>\template
    or
    C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\<category>\template_runtime
    to a directory of your own:
    C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\<category>\myproject
  2. Edit the filenames of the project to suit your needs.
  3. Edit the *.sln, *.vcproj and source files. Just search and replace all occurrences of template or template_runtime with myproject.
  4. Build the 32-bit and/or 64-bit, release or debug configurations using:
    • myproject_vs<version>.sln
  5. Run myproject.exe from the release or debug directories located in:
    C:\ProgramData\NVIDIA Corporation\CUDA Samples\v7.0\bin\win[32|64]\[release|debug]
  6. Now modify the code to perform the computation you require. See the CUDA Programming Guide for details of programming in CUDA.

2.4.2. Creating CUDA Projects for Linux

Note: The default installation folder <SAMPLES_INSTALL_PATH> is NVIDIA_CUDA_7.0_Samples and <category> is one of the following: 0_Simple, 1_Utilities, 2_Graphics, 3_Imaging, 4_Finance, 5_Simulations, 6_Advanced, 7_CUDALibraries.
Creating a new CUDA Program using the NVIDIA CUDA Samples infrastructure is easy. We have provided a template or template_runtime project that you can copy and modify to suit your needs. Just follow these steps:
  1. Copy the template or template_runtime project:
    cd <SAMPLES_INSTALL_PATH>/<category>
    cp -r template <myproject>
    or (using template_runtime):
    cd <SAMPLES_INSTALL_PATH>/<category>
    cp -r template_runtime <myproject>
  2. Edit the filenames of the project to suit your needs:
    mv template.cu myproject.cu
    mv template_kernel.cu myproject_kernel.cu
    mv template_gold.cpp myproject_gold.cpp
    or (using template_runtime):
    mv main.cu  myproject.cu
  3. Edit the Makefile and source files. Just search and replace all occurrences of template or template_runtime with myproject.
  4. Build the project as (release):
    make
    To build the project as (debug), use "make dbg=1":
    make dbg=1
  5. Run the program:
    ../../bin/x86_64/linux/release/myproject
  6. Now modify the code to perform the computation you require. See the CUDA Programming Guide for details of programming in CUDA.

2.4.3. Creating CUDA Projects for Mac OS X

Note: The default installation folder <SAMPLES_INSTALL_PATH> is: /Developer/NVIDIA/CUDA-7.0/samples

Creating a new CUDA Program using the NVIDIA CUDA Samples infrastructure is easy. We have provided a template project that you can copy and modify to suit your needs. Just follow these steps:

(<category> is one of the following: 0_Simple, 1_Utilities, 2_Graphics, 3_Imaging, 4_Finance, 5_Simulations, 6_Advanced, 7_CUDALibraries.)

  1. Copy the template project:
    cd <SAMPLES_INSTALL_PATH>/<category>
    cp -r template <myproject>
  2. Edit the filenames of the project to suit your needs:
    mv template.cu myproject.cu
    mv template_kernel.cu myproject_kernel.cu
    mv template_gold.cpp myproject_gold.cpp
  3. Edit the Makefile and source files. Just search and replace all occurrences of template with myproject.
  4. Build the project as (release):
    make
    Note: To build the project as (debug), use "make dbg=1"
    make dbg=1
  5. Run the program:
    ../../bin/x86_64/darwin/release/myproject
    (It should print PASSED.)
  6. Now modify the code to perform the computation you require. See the CUDA Programming Guide for details of programming in CUDA.

3. Samples Reference

This document contains a complete listing of the code samples that are included with the NVIDIA CUDA Toolkit. It describes each code sample, lists the minimum GPU specification, and provides links to the source code and white papers if available.

The code samples are divided into the following categories:
Simple Reference
Basic CUDA samples for beginners that illustrate key concepts with using CUDA and CUDA runtime APIs.
Utilities Reference
Utility samples that demonstrate how to query device capabilities and measure GPU/CPU bandwidth.
Graphics Reference
Graphical samples that demonstrate interoperability between CUDA and OpenGL or DirectX.
Imaging Reference
Samples that demonstrate image processing, compression, and data analysis.
Finance Reference
Samples that demonstrate parallel algorithms for financial computing.
Simulations Reference
Samples that illustrate a number of simulation algorithms implemented with CUDA.
Advanced Reference
Samples that illustrate advanced algorithms implemented with CUDA.
Cudalibraries Reference
Samples that illustrate how to use CUDA platform libraries (NPP, cuBLAS, cuFFT, cuSPARSE, and cuRAND).

3.1. Simple Reference

asyncAPI

This sample uses CUDA streams and events to overlap execution on CPU and GPU.

cdpSimplePrint - Simple Print (CUDA Dynamic Parallelism)

This sample demonstrates simple printf implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

cdpSimpleQuicksort - Simple Quicksort (CUDA Dynamic Parallelism)

This sample demonstrates simple quicksort implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

clock - Clock

This example shows how to use the clock function to measure the performance of kernel accurately.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy
Key Concepts Performance Strategies
Supported OSes Linux, Windows, OS X

clock_nvrtc - Clock libNVRTC

This example shows how to use the clock function using libNVRTC to measure the performance of kernel accurately.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy
Key Concepts Performance Strategies, Runtime Compilation
Supported OSes Linux, Windows, OS X

cppIntegration - C++ Integration

This example demonstrates how to integrate CUDA into an existing C++ application, i.e. the CUDA entry point on host side is only a function which is called from C++ code and only the file containing this function is compiled with nvcc. It also demonstrates that vector types can be used from cpp.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy
Supported OSes Linux, Windows, OS X

cppOverload

This sample demonstrates how to use C++ function overloading on the GPU.

cudaOpenMP

This sample demonstrates how to use OpenMP API to write an application for multiple GPUs.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies OpenMP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy
Key Concepts CUDA Systems Integration, OpenMP, Multithreading
Supported OSes Linux, Windows, OS X

inlinePTX - Using Inline PTX

A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel.

inlinePTX_nvrtc - Using Inline PTX with libNVRTC

A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

matrixMul - Matrix Multiplication (CUDA Runtime API Version)

This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication.

matrixMul_nvrtc - Matrix Multiplication with libNVRTC

This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

matrixMulCUBLAS - Matrix Multiplication (CUBLAS)

This sample implements matrix multiplication from Chapter 3 of the programming guide. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

matrixMulDrv - Matrix Multiplication (CUDA Driver API Version)

This sample implements matrix multiplication and uses the new CUDA 4.0 kernel launch Driver API. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. CUBLAS provides high-performance matrix multiplication.

simpleAssert

This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 .

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaMallocHost, cudaFree, cudaFreeHost, cudaMemcpy
Key Concepts Assert
Supported OSes Linux, Windows

simpleAssert_nvrtc - simpleAssert with libNVRTC

This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 .

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaMallocHost, cudaFree, cudaFreeHost, cudaMemcpy
Key Concepts Assert, Runtime Compilation
Supported OSes Linux, Windows

simpleAtomicIntrinsics - Simple Atomic Intrinsics

A simple demonstration of global memory atomic instructions. Requires Compute Capability 2.0 or higher.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy, cudaFreeHost
Key Concepts Atomic Intrinsics
Supported OSes Linux, Windows, OS X

simpleAtomicIntrinsics_nvrtc - Simple Atomic Intrinsics with libNVRTC

A simple demonstration of global memory atomic instructions.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaMemcpy, cudaFreeHost
Key Concepts Atomic Intrinsics, Runtime Compilation
Supported OSes Linux, Windows, OS X

simpleCallback - Simple CUDA Callbacks

This sample implements multi-threaded heterogeneous computing workloads with the new CPU callbacks for CUDA streams and events introduced with CUDA 5.0.

simpleCubemapTexture - Simple Cubemap Texture

Simple example that demonstrates how to use a new CUDA 4.1 feature to support cubemap Textures in CUDA C.

simpleIPC

This CUDA Runtime API sample is a very basic sample that demonstrates Inter Process Communication with one process per GPU for computation. Requires Compute Capability 2.0 or higher and a Linux Operating System

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleLayeredTexture - Simple Layered Texture

Simple example that demonstrates how to use a new CUDA 4.0 feature to support layered Textures in CUDA C.

simpleMPI

Simple example demonstrating how to use MPI in combination with CUDA.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies MPI
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMallco, cudaFree, cudaMemcpy
Key Concepts CUDA Systems Integration, MPI, Multithreading
Supported OSes Linux, Windows, OS X

simpleMultiCopy - Simple Multi Copy and Compute

Supported in GPUs with Compute Capability 1.1, overlapping compute with one memcopy is possible from the host system. For Quadro and Tesla GPUs with Compute Capability 2.0, a second overlapped copy operation in either direction at full speed is possible (PCI-e is symmetric). This sample illustrates the usage of CUDA streams to achieve overlapping of kernel execution with data copies to and from the device.

simpleMultiGPU - Simple Multi-GPU

This application demonstrates how to use the new CUDA 4.0 API for CUDA context management and multi-threaded access to run CUDA kernels on multiple-GPUs.

simpleOccupancy

This sample demonstrates the basic usage of the CUDA occupancy calculator and occupancy-based launch configurator APIs by launching a kernel with the launch configurator, and measures the utilization difference against a manually configured launch.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Occupancy Calculator
Supported OSes Linux, Windows, OS X

simpleP2P - Simple Peer-to-Peer Transfers with Multi-GPU

This application demonstrates the new CUDA 4.0 APIs that support Peer-To-Peer (P2P) copies, Peer-To-Peer (P2P) addressing, and UVA (Unified Virtual Memory Addressing) between multiple Tesla GPUs.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simplePrintf

This CUDA Runtime API sample is a very basic sample that implements how to use the printf function in the device code.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Debugging
Supported OSes Linux, Windows, OS X

simpleSeparateCompilation - Simple Static GPU Device Library

This sample demonstrates a CUDA 5.0 feature, the ability to create a GPU device static library and use it within another CUDA kernel. This example demonstrates how to pass in a GPU device function (from the GPU device static library) as a function pointer to be called. This sample requires devices with compute capability 2.0 or higher.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Separate Compilation
Supported OSes Linux, Windows, OS X

simpleStreams

This sample uses CUDA streams to overlap kernel executions with memory copies between the host and a GPU device. This sample uses a new CUDA 4.0 feature that supports pinning of generic host memory. Requires Compute Capability 2.0 or higher.

simpleSurfaceWrite - Simple Surface Write

Simple example that demonstrates the use of 2D surface references (Write-to-Texture)

simpleTemplates - Simple Templates

This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts C++ Templates
Supported OSes Linux, Windows, OS X

simpleTemplates_nvrtc - Simple Templates with libNVRTC

This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts C++ Templates, Runtime Compilation
Supported OSes Linux, Windows, OS X

simpleVoteIntrinsics - Simple Vote Intrinsics

Simple program which demonstrates how to use the Vote (any, all) intrinsic instruction in a CUDA kernel. Requires Compute Capability 2.0 or higher.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMallco, cudaFree, cudaMemcpy, cudaFreeHost
Key Concepts Vote Intrinsics
Supported OSes Linux, Windows, OS X

simpleVoteIntrinsics_nvrtc - Simple Vote Intrinsics with libNVRTC

Simple program which demonstrates how to use the Vote (any, all) intrinsic instruction in a CUDA kernel with runtime compilation using NVRTC APIs. Requires Compute Capability 2.0 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cuMemAlloc, cuMemFree, cuMemcpyHtoD, cuMemFree
Key Concepts Vote Intrinsics, CUDA Driver API, Runtime Compilation
Supported OSes Linux, Windows, OS X

simpleZeroCopy

This sample illustrates how to use Zero MemCopy, kernels can read and write directly to pinned system memory.

template - Template

A trivial template project that can be used as a starting point to create new CUDA projects.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cudaMalloc, cudaFree, cudaDeviceSynchronize, cudaMemcpy
Key Concepts Device Memory Allocation
Supported OSes Linux, Windows, OS X

template_runtime - Template using CUDA Runtime

A trivial template project that can be used as a starting point to create new CUDA Runtime API projects.

UnifiedMemoryStreams - Unified Memory Streams

This sample demonstrates the use of OpenMP and streams with Unified Memory on a single GPU.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

vectorAdd - Vector Addition

This CUDA Runtime API sample is a very basic sample that implements element by element vector addition. It is the same as the sample illustrating Chapter 3 of the programming guide with some additions like error checking.

vectorAdd_nvrtc - Vector Addition with libNVRTC

This CUDA Driver API sample uses NVRTC for runtime compilation of vector addition kernel. Vector addition kernel demonstrated is the same as the sample illustrating Chapter 3 of the programming guide.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cuMemAlloc, cuMemFree, cuMemcpyHtoD, cuMemcpyDtoH
Key Concepts CUDA Driver API, Vector Addition, Runtime Compilation
Supported OSes Linux, Windows, OS X

vectorAddDrv - Vector Addition Driver API

This Vector Addition sample is a basic sample that is implemented element by element. It is the same as the sample illustrating Chapter 3 of the programming guide with some additions like error checking. This sample also uses the new CUDA 4.0 kernel launch Driver API.

3.2. Utilities Reference

bandwidthTest - Bandwidth Test

This is a simple test program to measure the memcopy bandwidth of the GPU and memcpy bandwidth across PCI-e. This test application is capable of measuring device to device copy bandwidth, host to device copy bandwidth for pageable and page-locked memory, and device to host copy bandwidth for pageable and page-locked memory.

deviceQuery - Device Query

This sample enumerates the properties of the CUDA devices present in the system.

deviceQueryDrv - Device Query Driver API

This sample enumerates the properties of the CUDA devices present using CUDA Driver API calls

p2pBandwidthLatencyTest - Peer-to-Peer Bandwidth Latency Test with Multi-GPUs

This application demonstrates the CUDA Peer-To-Peer (P2P) data transfers between pairs of GPUs and computes latency and bandwidth. Tests on GPU pairs using P2P and without P2P are tested.

3.3. Graphics Reference

bindlessTexture - Bindless Texture

This example demonstrates use of cudaSurfaceObject, cudaTextureObject, and MipMap support in CUDA. A GPU with Compute Capability SM 3.0 is required to run the sample.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Mandelbrot

This sample uses CUDA to compute and display the Mandelbrot or Julia sets interactively. It also illustrates the use of "double single" arithmetic to improve precision when zooming a long way into the pattern. This sample uses double precision. Thanks to Mark Granger of NewTek who submitted this code sample.!

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

marchingCubes - Marching Cubes Isosurfaces

This sample extracts a geometric isosurface from a volume dataset using the marching cubes algorithm. It uses the scan (prefix sum) function from the Thrust library to perform stream compaction.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleD3D10 - Simple Direct3D10 (Vertex Array)

Simple program which demonstrates interoperability between CUDA and Direct3D10. The program generates a vertex array with CUDA and uses Direct3D10 to render the geometry. A Direct3D Capable device is required.

simpleD3D10RenderTarget - Simple Direct3D10 Render Target

Simple program which demonstrates interop of rendertargets between Direct3D10 and CUDA. The program uses RenderTarget positions with CUDA and generates a histogram with visualization. A Direct3D10 Capable device is required.

simpleD3D10Texture - Simple D3D10 Texture

Simple program which demonstrates how to interoperate CUDA with Direct3D10 Texture. The program creates a number of D3D10 Textures (2D, 3D, and CubeMap) which are generated from CUDA kernels. Direct3D then renders the results on the screen. A Direct3D10 Capable device is required.

simpleD3D11Texture - Simple D3D11 Texture

Simple program which demonstrates Direct3D11 Texture interoperability with CUDA. The program creates a number of D3D11 Textures (2D, 3D, and CubeMap) which are written to from CUDA kernels. Direct3D then renders the results on the screen. A Direct3D Capable device is required.

simpleD3D9 - Simple Direct3D9 (Vertex Arrays)

Simple program which demonstrates interoperability between CUDA and Direct3D9. The program generates a vertex array with CUDA and uses Direct3D9 to render the geometry. A Direct3D capable device is required.

simpleD3D9Texture - Simple D3D9 Texture

Simple program which demonstrates Direct3D9 Texture interoperability with CUDA. The program creates a number of D3D9 Textures (2D, 3D, and CubeMap) which are written to from CUDA kernels. Direct3D then renders the results on the screen. A Direct3D capable device is required.

simpleGL - Simple OpenGL

Simple program which demonstrates interoperability between CUDA and OpenGL. The program modifies vertex positions with CUDA and uses OpenGL to render the geometry.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleGLES - Simple OpenGLES

Demonstrates data exchange between CUDA and OpenGL ES (aka Graphics interop). The program modifies vertex positions with CUDA and uses OpenGL ES to render the geometry.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleGLES_screen - Simple OpenGLES on Screen

Demonstrates data exchange between CUDA and OpenGL ES (aka Graphics interop). The program modifies vertex positions with CUDA and uses OpenGL ES to render the geometry.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleTexture3D - Simple Texture 3D

Simple example that demonstrates use of 3D Textures in CUDA.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

SLID3D10Texture - SLI D3D10 Texture

Simple program which demonstrates SLI with Direct3D10 Texture interoperability with CUDA. The program creates a D3D10 Texture which is written to from a CUDA kernel. Direct3D then renders the results on the screen. A Direct3D Capable device is required.

volumeFiltering - Volumetric Filtering with 3D Textures and Surface Writes

This sample demonstrates 3D Volumetric Filtering using 3D Textures and 3D Surface Writes.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

volumeRender - Volume Rendering with 3D Textures

This sample demonstrates basic volume rendering using 3D Textures.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

3.4. Imaging Reference

bicubicTexture - Bicubic B-spline Interoplation

This sample demonstrates how to efficiently implement a Bicubic B-spline interpolation filter with CUDA texture.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

bilateralFilter - Bilateral Filter

Bilateral filter is an edge-preserving non-linear smoothing filter that is implemented with CUDA with OpenGL rendering. It can be used in image recovery and denoising. Each pixel is weight by considering both the spatial distance and color distance between its neighbors. Reference:"C. Tomasi, R. Manduchi, Bilateral Filtering for Gray and Color Images, proceeding of the ICCV, 1998, http://users.soe.ucsc.edu/~manduchi/Papers/ICCV98.pdf"

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

boxFilter - Box Filter

Fast image box filter using CUDA with OpenGL rendering.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

convolutionFFT2D - FFT-Based 2D Convolution

This sample demonstrates how 2D convolutions with very large kernel sizes can be efficiently implemented using FFT transformations.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUFFT
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cufftPlan2d, cufftExecR2C, cufftExecC2R, cufftDestroy
Key Concepts Image Processing, CUFFT Library
Supported OSes Linux, Windows, OS X

convolutionSeparable - CUDA Separable Convolution

This sample implements a separable convolution filter of a 2D signal with a gaussian kernel.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Data Parallel Algorithms
Supported OSes Linux, Windows, OS X
Whitepaper convolutionSeparable.pdf

convolutionTexture - Texture-based Separable Convolution

Texture-based implementation of a separable 2D convolution with a gaussian kernel. Used for performance comparison against convolutionSeparable.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Texture, Data Parallel Algorithms
Supported OSes Linux, Windows, OS X

cudaDecodeD3D9 - CUDA Video Decoder D3D9 API

This sample demonstrates how to efficiently use the CUDA Video Decoder API to decode MPEG-2, VC-1, or H.264 sources. YUV to RGB conversion of video is accomplished with CUDA kernel. The output result is rendered to a D3D9 surface. The decoded video is not displayed on the screen, but with -displayvideo at the command line parameter, the video output can be seen. Requires a Direct3D capable device and Compute Capability 2.0 or higher.

cudaDecodeGL - CUDA Video Decoder GL API

This sample demonstrates how to efficiently use the CUDA Video Decoder API to decode video sources based on MPEG-2, VC-1, and H.264. YUV to RGB conversion of video is accomplished with CUDA kernel. The output result is rendered to a OpenGL surface. The decoded video is black, but can be enabled with -displayvideo added to the command line. Requires Compute Capability 2.0 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

dct8x8 - DCT8x8

This sample demonstrates how Discrete Cosine Transform (DCT) for blocks of 8 by 8 pixels can be performed using CUDA: a naive implementation by definition and a more traditional approach used in many libraries. As opposed to implementing DCT in a fragment shader, CUDA allows for an easier and more efficient implementation.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Video Compression
Supported OSes Linux, Windows, OS X
Whitepaper dct8x8.pdf

dwtHaar1D - 1D Discrete Haar Wavelet Decomposition

Discrete Haar wavelet decomposition for 1D signals with a length which is a power of 2.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Video Compression
Supported OSes Linux, Windows, OS X

dxtc - DirectX Texture Compressor (DXTC)

High Quality DXT Compression using CUDA. This example shows how to implement an existing computationally-intensive CPU compression algorithm in parallel on the GPU, and obtain an order of magnitude performance improvement.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Image Compression
Supported OSes Linux, Windows, OS X
Whitepaper cuda_dxtc.pdf

histogram - CUDA Histogram

This sample demonstrates efficient implementation of 64-bin and 256-bin histogram.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Data Parallel Algorithms
Supported OSes Linux, Windows, OS X
Whitepaper histogram.pdf

HSOpticalFlow - Optical Flow

Variational optical flow estimation example. Uses textures for image operations. Shows how simple PDE solver can be accelerated with CUDA.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Data Parallel Algorithms
Supported OSes Linux, Windows, OS X
Whitepaper OpticalFlow.pdf

imageDenoising - Image denoising

This sample demonstrates two adaptive image denoising techniques: KNN and NLM, based on computation of both geometric and color distance between texels. While both techniques are implemented in the DirectX SDK using shaders, massively speeded up variation of the latter technique, taking advantage of shared memory, is implemented in addition to DirectX counterparts.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies X11, GL
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing
Supported OSes Linux, Windows, OS X
Whitepaper imageDenoising.pdf

postProcessGL - Post-Process in OpenGL

This sample shows how to post-process an image rendered in OpenGL using CUDA.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

recursiveGaussian - Recursive Gaussian Filter

This sample implements a Gaussian blur using Deriche's recursive method. The advantage of this method is that the execution time is independent of the filter width.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

simpleCUDA2GL - CUDA and OpenGL Interop of Images

This sample shows how to copy CUDA image back to OpenGL using the most efficient methods.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

SobelFilter - Sobel Filter

This sample implements the Sobel edge detection filter for 8-bit monochrome images.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

stereoDisparity - Stereo Disparity Computation (SAD SIMD Intrinsics)

A CUDA program that demonstrates how to compute a stereo disparity map using SIMD SAD (Sum of Absolute Difference) intrinsics. Requires Compute Capability 2.0 or higher.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Video Intrinsics
Supported OSes Linux, Windows, OS X

3.5. Finance Reference

binomialOptions - Binomial Option Pricing

This sample evaluates fair call price for a given set of European options under binomial model.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X
Whitepaper binomialOptions.pdf

binomialOptions_nvrtc - Binomial Option Pricing with libNVRTC

This sample evaluates fair call price for a given set of European options under binomial model. This sample makes use of NVRTC for Runtime Compilation.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X
Whitepaper binomialOptions.pdf

BlackScholes - Black-Scholes Option Pricing

This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X
Whitepaper BlackScholes.pdf

BlackScholes_nvrtc - Black-Scholes Option Pricing with libNVRTC

This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula, compiling the CUDA kernels involved at runtime using NVRTC.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X
Whitepaper BlackScholes.pdf

MonteCarloMultiGPU - Monte Carlo Option Pricing with Multi-GPU support

This sample evaluates fair call price for a given set of European options using the Monte Carlo approach, taking advantage of all CUDA-capable GPUs installed in the system. This sample use double precision hardware if a GTX 200 class GPU is present. The sample also takes advantage of CUDA 4.0 capability to supporting using a single CPU thread to control multiple GPUs

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Supported OSes Linux, Windows, OS X
Whitepaper MonteCarlo.pdf

quasirandomGenerator - Niederreiter Quasirandom Sequence Generator

This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X

quasirandomGenerator_nvrtc - Niederreiter Quasirandom Sequence Generator with libNVRTC

This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions, compiling the CUDA kernels involved at runtime using NVRTC.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies NVRTC
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance, Runtime Compilation
Supported OSes Linux, Windows, OS X

SobolQRNG - Sobol Quasirandom Number Generator

This sample implements Sobol Quasirandom Sequence Generator.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance
Supported OSes Linux, Windows, OS X

3.6. Simulations Reference

fluidsGL - Fluids (OpenGL Version)

An example of fluid simulation using CUDA and CUFFT, with OpenGL rendering.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

nbody - CUDA N-Body Simulation

This sample demonstrates efficient all-pairs simulation of a gravitational n-body simulation in CUDA. This sample accompanies the GPU Gems 3 chapter "Fast N-Body Simulation with CUDA". With CUDA 5.5, performance on Tesla K20c has increased to over 1.8TFLOP/s single precision. Double Performance has also improved on all Kepler and Fermi GPU architectures as well. Starting in CUDA 4.0, the nBody sample has been updated to take advantage of new features to easily scale the n-body simulation across multiple GPUs in a single PC. Adding "-numbodies=<bodies>" to the command line will allow users to set # of bodies for simulation. Adding “-numdevices=<N>” to the command line option will cause the sample to use N devices (if available) for simulation. In this mode, the position and velocity data for all bodies are read from system memory using “zero copy” rather than from device memory. For a small number of devices (4 or fewer) and a large enough number of bodies, bandwidth is not a bottleneck so we can achieve strong scaling across these devices.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

nbody_screen - CUDA N-Body Simulation on Screen

This sample demonstrates efficient all-pairs simulation of a gravitational n-body simulation in CUDA. Unlike the OpenGL nbody sample, there is no user interaction.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

oceanFFT - CUDA FFT Ocean Simulation

This sample simulates an Ocean height field using CUFFT Library and renders the result using OpenGL.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

particles - Particles

This sample uses CUDA to simulate and visualize a large set of particles and their physical interaction. Adding "-particles=<N>" to the command line will allow users to set # of particles for simulation. This example implements a uniform grid data structure using either atomic operations or a fast radix sort from the Thrust library

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

smokeParticles - Smoke Particles

Smoke simulation with volumetric shadows using half-angle slicing technique. Uses CUDA for procedural simulation, Thrust Library for sorting algorithms, and OpenGL for graphics rendering.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

VFlockingD3D10

The sample models formation of V-shaped flocks by big birds, such as geese and cranes. The algorithms of such flocking are borrowed from the paper "V-like formations in flocks of artificial birds" from Artificial Life, Vol. 14, No. 2, 2008. The sample has CPU- and GPU-based implementations. Press 'g' to toggle between them. The GPU-based simulation works many times faster than the CPU-based one. The printout in the console window reports the simulation time per step. Press 'r' to reset the initial distribution of birds.

3.7. Advanced Reference

alignedTypes - Aligned Types

A simple test, showing huge access speed gap between aligned and misaligned structures.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies
Supported OSes Linux, Windows, OS X

cdpAdvancedQuicksort - Advanced Quicksort (CUDA Dynamic Parallelism)

This sample demonstrates an advanced quicksort implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

cdpBezierTessellation - Bezier Line Tessellation (CUDA Dynamic Parallelism)

This sample demonstrates bezier tessellation of lines implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

cdpLUDecomposition - LU Decomposition (CUDA Dynamic Parallelism)

This sample demonstrates LU Decomposition implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP, CUBLAS
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

cdpQuadtree - Quad Tree (CUDA Dynamic Parallelism)

This sample demonstrates Quad Trees implemented using CUDA Dynamic Parallelism. This sample requires devices with compute capability 3.5 or higher.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Dynamic Parallelism
Supported OSes Linux, Windows, OS X

concurrentKernels - Concurrent Kernels

This sample demonstrates the use of CUDA streams for concurrent execution of several kernels on devices of compute capability 2.0 or higher. Devices of compute capability 1.x will run the kernels sequentially. It also illustrates how to introduce dependencies between CUDA streams with the new cudaStreamWaitEvent function introduced in CUDA 3.2

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies
Supported OSes Linux, Windows, OS X

eigenvalues - Eigenvalues

The computation of all or a subset of all eigenvalues is an important problem in Linear Algebra, statistics, physics, and many other fields. This sample demonstrates a parallel implementation of a bisection algorithm for the computation of all eigenvalues of a tridiagonal symmetric matrix of arbitrary size with CUDA.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra
Supported OSes Linux, Windows, OS X
Whitepaper eigenvalues.pdf

fastWalshTransform - Fast Walsh Transform

Naturally(Hadamard)-ordered Fast Walsh Transform for batching vectors of arbitrary eligible lengths that are power of two in size.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra, Data-Parallel Algorithms, Video Compression
Supported OSes Linux, Windows, OS X

FDTD3d - CUDA C 3D FDTD

This sample applies a finite differences time domain progression stencil on a 3D surface.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies
Supported OSes Linux, Windows, OS X

FunctionPointers - Function Pointers

This sample illustrates how to use function pointers and implements the Sobel Edge Detection filter for 8-bit monochrome images.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies X11, GL
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Graphics Interop, Image Processing
Supported OSes Linux, Windows, OS X

interval - Interval Computing

Interval arithmetic operators example. Uses various C++ features (templates and recursion). The recursive mode requires Compute SM 2.0 capabilities.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Recursion, Templates
Supported OSes Linux, Windows, OS X

lineOfSight - Line of Sight

This sample is an implementation of a simple line-of-sight algorithm: Given a height map and a ray originating at some observation point, it computes all the points along the ray that are visible from the observation point. The implementation is based on the Thrust library (http://code.google.com/p/thrust/).

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Supported OSes Linux, Windows, OS X

matrixMulDynlinkJIT - Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version)

This sample revisits matrix multiplication using the CUDA driver API. It demonstrates how to link to CUDA driver at runtime and how to use JIT (just-in-time) compilation from PTX code. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. CUBLAS provides high-performance matrix multiplication.

mergeSort - Merge Sort

This sample implements a merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient on large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), may be the algorithms of choice for sorting batches of short- to mid-sized (key, value) array pairs. Refer to the excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms
Supported OSes Linux, Windows, OS X

newdelete - NewDelete

This sample demonstrates dynamic global memory allocation through device C++ new and delete operators and virtual function declarations available with CUDA 4.0.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Supported OSes Linux, Windows, OS X

ptxjit - PTX Just-in-Time compilation

This sample uses the Driver API to just-in-time compile (JIT) a Kernel from PTX code. Additionally, this sample demonstrates the seamless interoperability capability of the CUDA Runtime and CUDA Driver API calls. For CUDA 5.5, this sample shows how to use cuLink* functions to link PTX assembly using the CUDA driver at runtime.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Driver API
Supported OSes Linux, Windows, OS X

radixSortThrust - CUDA Radix Sort (Thrust Library)

This sample demonstrates a very fast and efficient parallel radix sort uses Thrust library (http://code.google.com/p/thrust/). The included RadixSort class can sort either key-value pairs (with float or unsigned integer keys) or keys only. The optimized code in this sample (and also in reduction and scan) uses a technique known as warp-synchronous programming, which relies on the fact that within a warp of threads running on a CUDA GPU, all threads execute instructions synchronously. The code uses this to avoid __syncthreads() when threads within a warp are sharing data via __shared__ memory. It is important to note that for this to work correctly without race conditions on all GPUs, the shared memory used in these warp-synchronous expressions must be declared volatile. If it is not declared volatile, then in the absence of __syncthreads(), the compiler is free to delay stores to __shared__ memory and keep the data in registers (an optimization technique), which will result in incorrect execution. So please heed the use of volatile in these samples and use it in the same way in any code you derive from them.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X
Whitepaper readme.txt

reduction - CUDA Parallel Reduction

A parallel sum reduction that computes the sum of a large arrays of values. This sample demonstrates several important optimization strategies for 1:Data-Parallel Algorithms like reduction.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X

scalarProd - Scalar Product

This sample calculates scalar products of a given set of input vector pairs.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra
Supported OSes Linux, Windows, OS X

scan - CUDA Parallel Prefix Sum (Scan)

This example demonstrates an efficient CUDA implementation of parallel prefix sum, also known as "scan". Given an array of numbers, scan computes a new array in which each element is the sum of all the elements before it in the input array.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X

segmentationTreeThrust - CUDA Segmentation Tree Thrust Library

This sample demonstrates an approach to the image segmentation trees construction. This method is based on Boruvka's MST algorithm.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X

shfl_scan - CUDA Parallel Prefix Sum with Shuffle Intrinsics (SHFL_Scan)

This example demonstrates how to use the shuffle intrinsic __shfl_up to perform a scan operation across a thread block. A GPU with Compute Capability SM 3.0. is required to run the sample

Supported SM Architecture SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X

simpleHyperQ

This sample demonstrates the use of CUDA streams for concurrent execution of several kernels on devices which provide HyperQ (SM 3.5). Devices without HyperQ (SM 2.0 and SM 3.0) will run a maximum of two kernels concurrently.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Systems Integration, Performance Strategies
Supported OSes Linux, Windows, OS X
Whitepaper HyperQ.pdf

sortingNetworks - CUDA Sorting Networks

This sample implements bitonic sort and odd-even merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient, for large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), this may be the preferred algorithms of choice for sorting batches of short-sized to mid-sized (key, value) array pairs. Refer to an excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms
Supported OSes Linux, Windows, OS X

StreamPriorities - Stream Priorities

This sample demonstrates basic use of stream priorities.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies Stream-Priorities
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts CUDA Streams and Events
Supported OSes Linux

threadFenceReduction

This sample shows how to perform a reduction operation on an array of values using the thread Fence intrinsic to produce a single value in a single kernel (as opposed to two or more kernel calls as shown in the "reduction" CUDA Sample). Single-pass reduction requires global atomic instructions (Compute Capability 2.0 or later) and the _threadfence() intrinsic (CUDA 2.2 or later).

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Data-Parallel Algorithms, Performance Strategies
Supported OSes Linux, Windows, OS X

threadMigration - CUDA Context Thread Management

Simple program illustrating how to the CUDA Context Management API and uses the new CUDA 4.0 parameter passing and CUDA launch API. CUDA contexts can be created separately and attached independently to different threads.

transpose - Matrix Transpose

This sample demonstrates Matrix Transpose. Different performance are shown to achieve high performance.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies, Linear Algebra
Supported OSes Linux, Windows, OS X
Whitepaper MatrixTranspose.pdf

3.8. Cudalibraries Reference

batchCUBLAS

A CUDA Sample that demonstrates how using batched CUBLAS API calls to improve overall performance.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUBLAS
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra, CUBLAS Library
Supported OSes Linux, Windows, OS X

boxFilterNPP - Box Filter with NPP

A NPP CUDA Sample that demonstrates how to use NPP FilterBox function to perform a Box Filter.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies FreeImage, NPP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies, Image Processing, NPP Library
Supported OSes Linux, Windows, OS X

conjugateGradient - ConjugateGradient

This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUBLAS, CUSPARSE
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra, CUBLAS Library, CUSPARSE Library
Supported OSes Linux, Windows, OS X

conjugateGradientPrecond - Preconditioned Conjugate Gradient

This sample implements a preconditioned conjugate gradient solver on GPU using CUBLAS and CUSPARSE library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUBLAS, CUSPARSE
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Linear Algebra, CUBLAS Library, CUSPARSE Library
Supported OSes Linux, Windows, OS X

conjugateGradientUM - ConjugateGradientUM

This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library, using Unified Memory

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies UVM, CUBLAS, CUSPARSE
Supported SM Architecture SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Unified Memory, Linear Algebra, CUBLAS Library, CUSPARSE Library
Supported OSes Linux, Windows

cuHook - CUDA Interception Library

This sample demonstrates how to build and use an intercept library with CUDA.

Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Supported OSes Linux

freeImageInteropNPP - FreeImage and NPP Interopability

A simple CUDA Sample demonstrate how to use FreeImage library with NPP.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies FreeImage, NPP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies, Image Processing, NPP Library
Supported OSes Linux, Windows, OS X

grabcutNPP - GrabCut with NPP

CUDA Implementation of Rother et al. GrabCut approach using the 8 neighborhood NPP Graphcut primitive introduced in CUDA 4.1. (C. Rother, V. Kolmogorov, A. Blake. GrabCut: Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transactions on Graphics (SIGGRAPH'04), 2004)

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies X11, GL, FreeImage, NPP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Performance Strategies, Image Processing, NPP Library
Supported OSes Linux, Windows, OS X

histEqualizationNPP - Histogram Equalization with NPP

This CUDA Sample demonstrates how to use NPP for histogram equalization for image data.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies FreeImage, NPP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Performance Strategies, NPP Library
Supported OSes Linux, Windows, OS X

imageSegmentationNPP - Image Segmentation using Graphcuts with NPP

This sample that demonstrates how to perform image segmentation using the NPP GraphCut function.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies FreeImage, NPP
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, Performance Strategies, NPP Library
Supported OSes Linux, Windows, OS X

jpegNPP - JPEG encode/decode and resize with NPP

This sample demonstrates a simple image processing pipline. First, a JPEG file is huffman decoded and inverse DCT transformed and dequantized. Then the different plances are resized. Finally, the resized image is quantized, forward DCT transformed and huffman encoded.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

MC_EstimatePiInlineP - Monte Carlo Estimation of Pi (inline PRNG)

This sample uses Monte Carlo simulation for Estimation of Pi (using inline PRNG). This sample also uses the NVIDIA CURAND library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Random Number Generator, Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

MC_EstimatePiInlineQ - Monte Carlo Estimation of Pi (inline QRNG)

This sample uses Monte Carlo simulation for Estimation of Pi (using inline QRNG). This sample also uses the NVIDIA CURAND library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Random Number Generator, Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

MC_EstimatePiP - Monte Carlo Estimation of Pi (batch PRNG)

This sample uses Monte Carlo simulation for Estimation of Pi (using batch PRNG). This sample also uses the NVIDIA CURAND library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Random Number Generator, Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

MC_EstimatePiQ - Monte Carlo Estimation of Pi (batch QRNG)

This sample uses Monte Carlo simulation for Estimation of Pi (using batch QRNG). This sample also uses the NVIDIA CURAND library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Random Number Generator, Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

MC_SingleAsianOptionP - Monte Carlo Single Asian Option

This sample uses Monte Carlo to simulate Single Asian Options using the NVIDIA CURAND library.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Random Number Generator, Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

MersenneTwisterGP11213

This sample demonstrates the Mersenne Twister random number generator GP11213 in cuRAND.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Computational Finance, CURAND Library
Supported OSes Linux, Windows, OS X

randomFog - Random Fog

This sample illustrates pseudo- and quasi- random numbers produced by CURAND.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies X11, GL, CURAND
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts 3D Graphics, CURAND Library
Supported OSes Linux, Windows, OS X

simpleCUBLAS - Simple CUBLAS

Example of using CUBLAS using the new CUBLAS API interface available in CUDA 4.0.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUBLAS
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, CUBLAS Library
Supported OSes Linux, Windows, OS X

simpleCUFFT - Simple CUFFT

Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUFFT
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, CUFFT Library
Supported OSes Linux, Windows, OS X

simpleCUFFT_2d_MGPU - SimpleCUFFT_2d_MGPU

Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUFFT
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, CUFFT Library
Supported OSes Linux, Windows, OS X

simpleCUFFT_callback - Simple CUFFT Callbacks

Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain. The difference between this example and the Simple CUFFT example is that the multiplication step is done by the CUFFT kernel with a user-supplied CUFFT callback routine, rather than by a separate kernel call.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies callback, CUFFT
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, CUFFT Library
Supported OSes Linux

simpleCUFFT_MGPU - Simple CUFFT_MGPU

Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CUFFT
Supported SM Architecture SM 2.0, SM 3.0, SM 3.2, SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
Key Concepts Image Processing, CUFFT Library
Supported OSes Linux, Windows, OS X

simpleDevLibCUBLAS - simpleDevLibCUBLAS GPU Device API Library Functions (CUDA Dynamic Parallelism)

This sample implements a simple CUBLAS function calls that call GPU device API library running CUBLAS functions. This sample requires a SM 3.5 capable device.

This sample depends on other applications or libraries to be present on the system to either build or run. If these dependencies are not available on the system, the sample will not be installed. If these dependencies are available, but not installed, the sample will waive itself at build time.

Dependencies CDP, CUBLAS
Supported SM Architecture SM 3.5, SM 3.7, SM 5.0, SM 5.2, SM 5.3
CUDA API cublasCreate, cublasSetVector, cublasSgemm, cudaMalloc, cudaFree, cudaMemcpy
Key Concepts CUDA Dynamic Parallelism, Linear Algebra
Supported OSes Linux, Windows, OS X

4. Dependencies

Some CUDA Samples rely on third-party applications and/or libraries, or features provided by the CUDA Toolkit and Driver, to either build or execute. These dependencies are listed below.

If a sample has a dependency that is not available on the system, the sample will not be installed. If a sample has a third-party dependency that is available on the system, but is not installed, the sample will waive itself at build time.

Each sample's dependencies are listed in the Samples Reference section.

Third-Party Dependencies

These third-party dependencies are required by some CUDA samples. If available, these dependencies are either installed on your system automatically, or are installable via your system's package manager (Linux) or a third-party website.

FreeImage

FreeImage is an open source imaging library. FreeImage can usually be installed on Linux using your distribution's package manager system. FreeImage can also be downloaded from the FreeImage website. FreeImage is also redistributed with the CUDA Samples.

Message Passing Interface

MPI (Message Passing Interface) is an API for communicating data between distributed processes. A MPI compiler can be installed using your Linux distribution's package manager system. It is also available on some online resources, such as Open MPI.

Only 64-Bit

Some samples can only be run on a 64-bit operating system.

DirectX

DirectX is a collection of APIs designed to allow development of multimedia applications on Microsoft platforms. For Microsoft platforms, NVIDIA's CUDA Driver supports DirectX. Several CUDA Samples for Windows demonstrates CUDA-DirectX Interoperability, for building such samples one needs to install Direct X SDK (June 2010 or newer) , this is required to be installed only on Windows 7 and Windows Server 2008, Other Windows OSes do not need to explicitly install the DirectX SDK.

Samples which require the DirectX SDK include: simpleD3D9, simpleD3D9Texture, simpleD3D10, simpleD3D10RenderTarget, simpleD3D10Texture, simpleD3D11Texture, SLID3D10Texture, cudaDecodeD3D9, fluidsD3D9, and VFlockingD3D10.

OpenGL

OpenGL is a graphics library used for 2D and 3D rendering. On systems which support OpenGL, NVIDIA's OpenGL implementation is provided with the CUDA Driver.

OpenGL ES

OpenGL ES is an embedded systems graphics library used for 2D and 3D rendering. On systems which support OpenGL ES, NVIDIA's OpenGL ES implementation is provided with the CUDA Driver.

OpenMP

OpenMP is an API for multiprocessing programming. OpenMP can be installed using your Linux distribution's package manager system. It usually comes preinstalled with GCC. It can also be found at the OpenMP website.

Screen

Screen is a windowing system found on the QNX operating system. Screen is usually found as part of the root filesystem.

X11

X11 is a windowing system commonly found on *-nix style operating systems. X11 can be installed using your Linux distribution's package manager, and comes preinstalled on Mac OS X systems.

CUDA Features

These CUDA features are needed by some CUDA samples. They are provided by either the CUDA Toolkit or CUDA Driver. Some features may not be available on your system.

CUFFT Callback Routines

CUFFT Callback Routines are user-supplied kernel routines that CUFFT will call when loading or storing data. These callback routines are only available on Linux x86_64 and ppc64le systems.

CUDA Dynamic Paralellism

CDP (CUDA Dynamic Paralellism) allows kernels to be launched from threads running on the GPU. CDP is only available on GPUs with SM architecture of 3.5 or above.

CUBLAS

CUBLAS (CUDA Basic Linear Algebra Subroutines) is a GPU-accelerated version of the BLAS library.

CUDA Interprocess Communication

IPC (Interprocess Communication) allows processes to share device pointers. IPC is only available on Linux x86_64 and ppc64le systems.

CUFFT

CUFFT (CUDA Fast Fourier Transform) is a GPU-accelerated FFT library.

CURAND

CURAND (CUDA Random Number Generation) is a GPU-accelerated RNG library.

CUSPARSE

CUSPARSE (CUDA Sparse Matrix) provides linear algebra subroutines used for sparse matrix calculations.

NPP

NPP (NVIDIA Performance Primitives) provides GPU-accelerated image, video, and signal processing functions.

NVRTC

NVRTC (CUDA RunTime Compilation) is a runtime compilation library for CUDA C++.

NVCUVID

NVCUVID (NVIDIA CUDA Video Decoder) provides GPU-accelerated video decoding capabilities.

Stream Priorities

Stream Priorities allows the creation of streams with specified priorities. Stream Priorities is only available on GPUs with SM architecture of 3.5 or above.

Unified Virtual Memory

UVM (Unified Virtual Memory) enables memory that can be accessed by both the CPU and GPU without explicit copying between the two. UVM is only available on Linux and Windows systems.

5. Key Concepts and Associated Samples

The tables below describe the key concepts of the CUDA Toolkit and lists the samples that illustrate how that concept is used.

Basic Key Concepts

Basic Concepts demonstrates how to make use of CUDA features.

Table 2. Basic Key Concepts and Associated Samples
Basic Key Concept Description Samples

3D Graphics

3D Rendering

Random Fog, Simple Direct3D10 (Vertex Array), Simple OpenGL, Simple OpenGLES, Simple OpenGLES on Screen

3D Textures

Volume Textures

Simple Texture 3D

Assert

GPU Assert

simpleAssert, simpleAssert with libNVRTC

Asynchronous Data Transfers

Overlapping I/O and Compute

Peer-to-Peer Bandwidth Latency Test with Multi-GPUs, Simple Multi Copy and Compute, Simple Multi-GPU, Simple Peer-to-Peer Transfers with Multi-GPU, asyncAPI, simpleStreams

Atomic Intrinsics

Using atomics with GPU kernels

Simple Atomic Intrinsics, Simple Atomic Intrinsics with libNVRTC

C++ Function Overloading

Use C++ overloading with GPU kernels

cppOverload

C++ Templates

Using Templates with GPU kernels

Simple Templates, Simple Templates with libNVRTC

CUBLAS

CUDA BLAS samples

Matrix Multiplication (CUBLAS), Unified Memory Streams

CUBLAS Library

CUDA BLAS samples

Simple CUBLAS, batchCUBLAS

CUDA Data Transfers

CUDA Data I/O

Template using CUDA Runtime

CUDA Driver API

Samples that show the CUDA Driver API

Device Query Driver API, Matrix Multiplication (CUDA Driver API Version), Simple Texture (Driver Version), Simple Vote Intrinsics with libNVRTC, Using Inline PTX, Using Inline PTX with libNVRTC, Vector Addition Driver API, Vector Addition with libNVRTC

CUDA Dynamic Parallelism

Dynamic Parallelism with GPU Kernels (SM 3.5)

Simple Print (CUDA Dynamic Parallelism), simpleDevLibCUBLAS GPU Device API Library Functions (CUDA Dynamic Parallelism)

CUDA Runtime API

Samples that use the Runtime API

Device Query, Matrix Multiplication (CUBLAS), Matrix Multiplication (CUDA Runtime API Version), Matrix Multiplication with libNVRTC, Simple Texture, Vector Addition

CUDA Streams

Stream API definies a sequence of operations that can be overlapped with I/O

Simple CUDA Callbacks

CUDA Streams and Events

Synchronizing Kernels with Event Timers and Streams

Bandwidth Test, Simple Multi Copy and Compute, Simple Multi-GPU, Unified Memory Streams, asyncAPI, cppOverload, simpleStreams

CUDA Systems Integration

Samples that integrate with Multi Process (OpenMP, IPC, and MPI)

Unified Memory Streams, cudaOpenMP, simpleIPC, simpleMPI

CUFFT Library

Samples that use the CUDA FFT accelerated library

Simple CUFFT, Simple CUFFT Callbacks, Simple CUFFT_MGPU, SimpleCUFFT_2d_MGPU

CURAND Library

Samples that use the CUDA random number generator

MersenneTwisterGP11213, Random Fog

Callback Functions

Creating Callback functions with GPU kernels

Simple CUDA Callbacks

Computational Finance

Finance Algorithms

Black-Scholes Option Pricing, Black-Scholes Option Pricing with libNVRTC, MersenneTwisterGP11213

Data Parallel Algorithms

Samples that show good usage of Data Parallel Algorithms

CUDA Separable Convolution, Texture-based Separable Convolution

Debugging

Samples useful for debugging

simplePrintf

Device Memory Allocation

Samples that show GPU Device side memory allocation

Template, Template using CUDA Runtime

Device Query

Sample showing simple device query of information

Device Query, Device Query Driver API

GPU Performance

Samples demonstrating high performance and data I/O

Simple Multi Copy and Compute

Graphics Interop

Samples that demonstrate interop between graphics APIs and CUDA

Bicubic B-spline Interoplation, Bilateral Filter, Box Filter, CUDA and OpenGL Interop of Images, Simple D3D10 Texture, Simple D3D11 Texture, Simple D3D9 Texture, Simple Direct3D10 (Vertex Array), Simple Direct3D10 Render Target, Simple Direct3D9 (Vertex Arrays), Simple OpenGL, Simple OpenGLES, Simple OpenGLES on Screen, Simple Texture 3D

Image Processing

Samples that demonstrate image processing algorithms in CUDA

Bicubic B-spline Interoplation, Bilateral Filter, Box Filter, Box Filter with NPP, CUDA Separable Convolution, CUDA and OpenGL Interop of Images, FreeImage and NPP Interopability, GrabCut with NPP, Histogram Equalization with NPP, Image Segmentation using Graphcuts with NPP, Pitch Linear Texture, Simple CUBLAS, Simple CUFFT, Simple CUFFT Callbacks, Simple CUFFT_MGPU, Simple D3D11 Texture, Simple Surface Write, Simple Texture, Simple Texture (Driver Version), Simple Texture 3D, SimpleCUFFT_2d_MGPU, Texture-based Separable Convolution

InterProcess Communication

Samples that demonstrate Inter Process Communication between processes

simpleIPC

Linear Algebra

Samples demonstrating linear algebra with CUDA

Matrix Multiplication (CUBLAS), Matrix Multiplication (CUDA Runtime API Version), Matrix Multiplication with libNVRTC, batchCUBLAS, simpleDevLibCUBLAS GPU Device API Library Functions (CUDA Dynamic Parallelism)

MPI

Samples demonstrating how to use CUDA with MPI programs

simpleMPI

Matrix Multiply

Samples demonstrating matrix multiply CUDA

Matrix Multiplication (CUDA Driver API Version)

Multi-GPU

Samples demonstrating how to take advantage of multiple GPUs and CUDA

Peer-to-Peer Bandwidth Latency Test with Multi-GPUs, Simple Multi-GPU, Simple Peer-to-Peer Transfers with Multi-GPU

Multithreading

Samples demonstrating how to use multithreading with CUDA

Simple CUDA Callbacks, Simple Multi-GPU, Unified Memory Streams, cudaOpenMP, simpleMPI

NPP Library

Samples demonstrating how to use NPP (NVIDIA Performance Primitives) for image processing

Box Filter with NPP, FreeImage and NPP Interopability, GrabCut with NPP, Histogram Equalization with NPP, Image Segmentation using Graphcuts with NPP

Occupancy Calculator

simpleOccupancy

OpenMP

Samples demonstrating how to use OpenMP

Unified Memory Streams, cudaOpenMP

Overlap Compute and Copy

Samples demonstrating how to overlap Compute and Data I/O

Simple Multi Copy and Compute

PTX Assembly

Samples demonstrating how to use PTX code with CUDA

Using Inline PTX, Using Inline PTX with libNVRTC

Peer to Peer

Samples demonstrating how to handle P2P data transfers between multiple GPUs

simpleIPC

Peer to Peer Data Transfers

Samples demonstrating how to handle P2P data transfers between multiple GPUs

Peer-to-Peer Bandwidth Latency Test with Multi-GPUs, Simple Peer-to-Peer Transfers with Multi-GPU

Performance Strategies

Samples demonstrating high performance with CUDA

Bandwidth Test, Box Filter with NPP, CUDA and OpenGL Interop of Images, Clock, Clock libNVRTC, FreeImage and NPP Interopability, GrabCut with NPP, Histogram Equalization with NPP, Image Segmentation using Graphcuts with NPP, Matrix Multiplication (CUBLAS), Peer-to-Peer Bandwidth Latency Test with Multi-GPUs, Simple Peer-to-Peer Transfers with Multi-GPU, Using Inline PTX, Using Inline PTX with libNVRTC, simpleZeroCopy

Pinned System Paged Memory

Samples demonstrating how to properly handle data I/O efficiently between the CPU host and GPU video memory

simpleZeroCopy

Separate Compilation

Samples demonstrating how to use CUDA library linking

Simple Static GPU Device Library

Surface Writes

Samples demonstrating how to use Surface Writes with GPU kernels

Simple Surface Write, Simple Texture 3D

Texture

Samples demonstrating how to use textures GPU kernels

Pitch Linear Texture, Simple Cubemap Texture, Simple D3D10 Texture, Simple D3D9 Texture, Simple Direct3D10 Render Target, Simple Layered Texture, Simple Surface Write, Simple Texture, Simple Texture (Driver Version), Texture-based Separable Convolution

Unified Memory

Samples demonstrating how to use Unified Memory

ConjugateGradientUM, Unified Memory Streams

Unified Virtual Address Space

Samples demonstrating how to use UVA with CUDA programs

Peer-to-Peer Bandwidth Latency Test with Multi-GPUs, Simple Peer-to-Peer Transfers with Multi-GPU

Vector Addition

Samples demonstrating how to use Vector Addition with CUDA programs

Vector Addition, Vector Addition Driver API, Vector Addition with libNVRTC, simpleZeroCopy

Vertex Buffers

Samples demonstrating how to use Vertex Buffers with CUDA kernels

Simple OpenGL, Simple OpenGLES, Simple OpenGLES on Screen

Volume Processing

Samples demonstrating how to use 3D Textures for volume rendering

Simple Cubemap Texture, Simple Layered Texture

Vote Intrinsics

Samples demonstrating how to use vote intrinsics with CUDA

Simple Vote Intrinsics, Simple Vote Intrinsics with libNVRTC

Advanced Key Concepts

Advanced Concepts demonstrate advanced techniques and algorithms implemented with CUDA.

Table 3. Advanced Key Concepts and Associated Samples
Advanced Key Concept Description Samples

2D Textures

Texture Mapping

SLI D3D10 Texture

3D Graphics

3D Rendering

Marching Cubes Isosurfaces

3D Textures

Volume Textures

Volume Rendering with 3D Textures, Volumetric Filtering with 3D Textures and Surface Writes

CUBLAS Library

CUDA BLAS samples

ConjugateGradient, ConjugateGradientUM, Preconditioned Conjugate Gradient

CUDA Driver API

Samples that show the CUDA Driver API

CUDA Context Thread Management, Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), PTX Just-in-Time compilation

CUDA Dynamic Parallelism

Dynamic Parallelism with GPU Kernels (SM 3.5)

Advanced Quicksort (CUDA Dynamic Parallelism), Bezier Line Tessellation (CUDA Dynamic Parallelism), LU Decomposition (CUDA Dynamic Parallelism), Quad Tree (CUDA Dynamic Parallelism), Simple Quicksort (CUDA Dynamic Parallelism)

CUDA Dynamically Linked Library

Dynamic loading of the CUDA DLL using CUDA Driver API

Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version)

CUDA Streams and Events

Synchronizing Kernels with Event Timers and Streams

Stream Priorities

CUDA Systems Integration

Samples that integrate with Multi Process (OpenMP, IPC, and MPI)

simpleHyperQ

CUFFT Library

Samples that use the CUDA FFT accelerated library

CUDA FFT Ocean Simulation, FFT-Based 2D Convolution, Fluids (Direct3D Version), Fluids (OpenGL Version)

CURAND Library

Samples that use the CUDA random number generator

Monte Carlo Estimation of Pi (batch PRNG), Monte Carlo Estimation of Pi (batch QRNG), Monte Carlo Estimation of Pi (inline PRNG), Monte Carlo Estimation of Pi (inline QRNG) , Monte Carlo Single Asian Option

CUSPARSE Library

Samples that use the cuSPARSE (Sparse Vector Matrix Multiply) functions

ConjugateGradient, ConjugateGradientUM, Preconditioned Conjugate Gradient

Computational Finance

Finance Algorithms

Binomial Option Pricing, Binomial Option Pricing with libNVRTC, Monte Carlo Estimation of Pi (batch PRNG), Monte Carlo Estimation of Pi (batch QRNG), Monte Carlo Estimation of Pi (inline PRNG), Monte Carlo Estimation of Pi (inline QRNG) , Monte Carlo Single Asian Option, Niederreiter Quasirandom Sequence Generator, Niederreiter Quasirandom Sequence Generator with libNVRTC, Sobol Quasirandom Number Generator

Data Parallel Algorithms

Samples that show good usage of Data Parallel Algorithms

CUDA Histogram, CUDA N-Body Simulation, CUDA N-Body Simulation on Screen, Mandelbrot, Optical Flow, Particles, Smoke Particles, VFlockingD3D10

Data-Parallel Algorithms

Samples that show good usage of Data Parallel Algorithms

CUDA Parallel Prefix Sum (Scan), CUDA Parallel Prefix Sum with Shuffle Intrinsics (SHFL_Scan), CUDA Parallel Reduction, CUDA Radix Sort (Thrust Library), CUDA Segmentation Tree Thrust Library, CUDA Sorting Networks, Fast Walsh Transform, Merge Sort, threadFenceReduction

Graphics Interop

Samples that demonstrate interop between graphics APIs and CUDA

Bindless Texture, CUDA FFT Ocean Simulation, CUDA N-Body Simulation, CUDA N-Body Simulation on Screen, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Fluids (Direct3D Version), Fluids (OpenGL Version), Function Pointers, Mandelbrot, Particles, Post-Process in OpenGL, Recursive Gaussian Filter, SLI D3D10 Texture, Smoke Particles, Sobel Filter, VFlockingD3D10, Volume Rendering with 3D Textures, Volumetric Filtering with 3D Textures and Surface Writes

Image Compression

Samples that demonstrate image and video compression

DirectX Texture Compressor (DXTC)

Image Processing

Samples that demonstrate image processing algorithms in CUDA

1D Discrete Haar Wavelet Decomposition, CUDA FFT Ocean Simulation, CUDA Histogram, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, DCT8x8, DirectX Texture Compressor (DXTC), FFT-Based 2D Convolution, Function Pointers, Image denoising, Optical Flow, Post-Process in OpenGL, Recursive Gaussian Filter, SLI D3D10 Texture, Sobel Filter, Stereo Disparity Computation (SAD SIMD Intrinsics), Volume Rendering with 3D Textures, Volumetric Filtering with 3D Textures and Surface Writes

Linear Algebra

Samples demonstrating linear algebra with CUDA

ConjugateGradient, ConjugateGradientUM, Eigenvalues, Fast Walsh Transform, Matrix Transpose, Preconditioned Conjugate Gradient, Scalar Product

OpenGL Graphics Interop

Samples demonstrating how to use interoperability CUDA with OpenGL

Marching Cubes Isosurfaces

Performance Strategies

Samples demonstrating high performance with CUDA

Aligned Types, CUDA C 3D FDTD, CUDA Parallel Prefix Sum (Scan), CUDA Parallel Prefix Sum with Shuffle Intrinsics (SHFL_Scan), CUDA Parallel Reduction, CUDA Radix Sort (Thrust Library), CUDA Segmentation Tree Thrust Library, Concurrent Kernels, Matrix Transpose, Particles, SLI D3D10 Texture, VFlockingD3D10, simpleHyperQ, threadFenceReduction

Physically Based Simulation

Samples demonstrating high performance collisions and/or physocal interactions

Marching Cubes Isosurfaces

Physically-Based Simulation

Samples demonstrating high performance collisions and/or physocal interactions

CUDA N-Body Simulation, CUDA N-Body Simulation on Screen, Fluids (Direct3D Version), Fluids (OpenGL Version), Particles, Smoke Particles, VFlockingD3D10

Random Number Generator

Samples demonstrating how to use random number generation with CUDA

Monte Carlo Estimation of Pi (batch PRNG), Monte Carlo Estimation of Pi (batch QRNG), Monte Carlo Estimation of Pi (inline PRNG), Monte Carlo Estimation of Pi (inline QRNG) , Monte Carlo Single Asian Option

Recursion

Samples demonstrating recursion on CUDA

Interval Computing

Runtime Compilation

Clock libNVRTC, Matrix Multiplication with libNVRTC, Niederreiter Quasirandom Sequence Generator with libNVRTC, Simple Atomic Intrinsics with libNVRTC, Simple Templates with libNVRTC, Simple Vote Intrinsics with libNVRTC, Using Inline PTX with libNVRTC, Vector Addition with libNVRTC, simpleAssert with libNVRTC

Surface Writes

Samples demonstrating how to use Surface Writes with GPU kernels

Volumetric Filtering with 3D Textures and Surface Writes

Templates

Samples demonstrating how to use templates GPU kernels

Interval Computing

Texture

Samples demonstrating how to use textures GPU kernels

Bindless Texture

Vertex Buffers

Samples demonstrating how to use Vertex Buffers with CUDA kernels

Marching Cubes Isosurfaces

Video Compression

Samples demonstrating how to use video compression with CUDA

1D Discrete Haar Wavelet Decomposition, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, DCT8x8, Fast Walsh Transform

Video Intrinsics

Samples demonstrating how to use video intrinsics with CUDA

Stereo Disparity Computation (SAD SIMD Intrinsics)

6. CUDA API and Associated Samples

The tables below list the samples associated with each CUDA API.

CUDA Driver API Samples

The table below lists the samples associated with each CUDA Driver API.

Table 4. CUDA Driver API and Associated Samples
CUDA Driver API Samples

cuArrayCreate

Simple Texture (Driver Version)

cuArrayDestroy

Simple Texture (Driver Version)

cuCtxCreate

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxDestroy

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxDetach

Simple Texture (Driver Version)

cuCtxPopCurrent

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxPushCurrent

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxSynchronize

Simple Texture (Driver Version)

cuD3D9CtxCreate

CUDA Video Decoder D3D9 API

cuD3D9GetDevice

CUDA Video Decoder D3D9 API

cuD3D9MapResources

CUDA Video Decoder D3D9 API

cuD3D9RegisterResource

CUDA Video Decoder D3D9 API

cuD3D9ResourceGetMappedPitch

CUDA Video Decoder D3D9 API

cuD3D9ResourceGetMappedPointer

CUDA Video Decoder D3D9 API

cuD3D9ResourceSetMapFlags

CUDA Video Decoder D3D9 API

cuD3D9UnmapResources

CUDA Video Decoder D3D9 API

cuD3D9UnregisterResource

CUDA Video Decoder D3D9 API

cuDeviceComputeCapability

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGet

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuDeviceGetAttribute

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGetCount

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGetName

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuDeviceTotalMem

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDriverGetVersion

Device Query Driver API

cuGLCtxCreate

CUDA Video Decoder GL API

cuGLGetDevice

CUDA Video Decoder GL API

cuGLMapResources

CUDA Video Decoder GL API

cuGLRegisterResource

CUDA Video Decoder GL API

cuGLResourceGetMappedPitch

CUDA Video Decoder GL API

cuGLResourceGetMappedPointer

CUDA Video Decoder GL API

cuGLResourceSetMapFlags

CUDA Video Decoder GL API

cuGLUnmapResources

CUDA Video Decoder GL API

cuGLUnregisterResource

CUDA Video Decoder GL API

cuInit

Device Query Driver API

cuLaunchGridAsync

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuLaunchKernel

CUDA Context Thread Management, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuMemAlloc

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuMemAllocHost

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuMemFree

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuMemFreeHost

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuMemcpy2D

Simple Texture (Driver Version)

cuMemcpyDtoH

CUDA Context Thread Management, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuMemcpyDtoHAsync

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuMemcpyHtoD

Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Vector Addition Driver API

cuMemsetD8

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuModuleGetFunction

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuModuleGetGlobal

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuModuleGetTexRef

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Simple Texture (Driver Version)

cuModuleLoad

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuModuleLoadDataEx

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Matrix Multiplication (CUDA Driver API Version), Matrix Multiplication (CUDA Driver API version with Dynamic Linking Version), Simple Texture (Driver Version), Vector Addition Driver API

cuModuleUnload

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuParamSetSize

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuParamSetTexRef

Simple Texture (Driver Version)

cuParamSeti

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuParamSetv

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuStreamCreate

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuTexRefSetAddressMode

Simple Texture (Driver Version)

cuTexRefSetArray

Simple Texture (Driver Version)

cuTexRefSetFilterMode

Simple Texture (Driver Version)

cuTexRefSetFlags

Simple Texture (Driver Version)

cuTexRefSetFormat

Simple Texture (Driver Version)

cuvidCreateDecoder

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidCtxLockCreate

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidCtxLockDestroy

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidDecodePicture

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidDestroyDecoder

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidMapVideoFrame

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuvidUnmapVideoFrame

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

CUDA Runtime API Samples

The table below lists the samples associated with each CUDA Runtime API.

Table 5. CUDA Runtime API and Associated Samples
CUDA Runtime API Samples

cuArrayCreate

Simple Texture (Driver Version)

cuArrayDestroy

Simple Texture (Driver Version)

cuCtxCreate

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxDestroy

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxDetach

Simple Texture (Driver Version)

cuCtxPopCurrent

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxPushCurrent

CUDA Context Thread Management, CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuCtxSynchronize

Simple Texture (Driver Version)

cuD3D9CtxCreate

CUDA Video Decoder D3D9 API

cuD3D9GetDevice

CUDA Video Decoder D3D9 API

cuD3D9MapResources

CUDA Video Decoder D3D9 API

cuD3D9RegisterResource

CUDA Video Decoder D3D9 API

cuD3D9ResourceGetMappedPitch

CUDA Video Decoder D3D9 API

cuD3D9ResourceGetMappedPointer

CUDA Video Decoder D3D9 API

cuD3D9ResourceSetMapFlags

CUDA Video Decoder D3D9 API

cuD3D9UnmapResources

CUDA Video Decoder D3D9 API

cuD3D9UnregisterResource

CUDA Video Decoder D3D9 API

cuDeviceComputeCapability

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGet

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuDeviceGetAttribute

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGetCount

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDeviceGetName

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API

cuDeviceTotalMem

CUDA Video Decoder D3D9 API, CUDA Video Decoder GL API, Device Query Driver API

cuDriverGetVersion

Device Query Driver API

cuGLCtxCreate

CUDA Video Decoder GL API