Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports CUDA Toolkit 12.8.
This section describes the release notes for the CUDA Samples on GitHub only.
Download and install the CUDA Toolkit 12.8 for your corresponding platform. For system requirements and installation instructions of cuda toolkit, please refer to the Linux Installation Guide, and the Windows Installation Guide.
Using git clone the repository of CUDA Samples using the command below.
git clone https://github.com/NVIDIA/cuda-samples.git
Without using git the easiest way to use these samples is to download the zip file containing the current version by clicking the "Download ZIP" button on the repo page. You can then unzip the entire archive and use the samples.
The CUDA Samples are built using CMake. Follow the instructions below for building on Linux, Windows, and for cross-compilation to Tegra devices.
Ensure that CMake (version 3.20 or later) is installed. Install it using your package manager if necessary:
e.g.
sudo apt install cmake
Navigate to the root of the cloned repository and create a build directory:
mkdir build && cd build
Configure the project with CMake:
cmake ..
Build the samples:
make -j$(nproc)
Run the samples from their respective directories in the build folder. You can also follow this process from and subdirectory of the samples repo, or from within any individual sample.
Language services for CMake are available in Visual Studio 2019 version 16.5 or later, and you can directly import the CUDA samples repository from either the root level or from any subdirectory or individual sample.
To build from the command line, open the x64 Native Tools Command Prompt for VS provided with your Visual Studio installation.
Navigate to the root of the cloned repository and create a build directory:
mkdir build && cd build
Configure the project with CMake - for example:
cmake .. -G "Visual Studio 16 2019" -A x64
Open the generated solution file CUDA_Samples.sln in Visual Studio. Build the samples by selecting the desired configuration (e.g., Debug or Release) and pressing F7 (Build Solution).
Run the samples from the output directories specified in Visual Studio.
Some CUDA samples are specific to certain platforms, and require passing flags into CMake to enable. In particular, we define the following platform-specific flags:
BUILD_TEGRA- for Tegra-specific samples
To build these samples, set the variables either on the command line or through your CMake GUI. For example:
cmake -DBUILD_TEGRA=True ..
Install the NVIDIA toolchain and cross-compilation environment for Tegra devices as described in the Tegra Development Guide.
Ensure that CMake (version 3.20 or later) is installed.
Navigate to the root of the cloned repository and create a build directory:
mkdir build && cd build
Configure the project with CMake, specifying the Tegra toolchain file:
cmake .. -DCMAKE_TOOLCHAIN_FILE=/path/to/tegra/toolchain.cmake
Build the samples:
make -j$(nproc)
Transfer the built binaries to the Tegra device and execute them there.
These platforms require additional information to be passed to CMake on the command line to ensure proper resolution of all necessary include and library files.
Instead of being in the default location, /usr/local/cuda/include or /usr/local/cuda/lib64, you must point to architecture-specific paths:
/usr/local/cuda/<ARCH>/targets/aarch64-linux/lib
and
/usr/local/cuda-12.8/<ARCH>/include
An example build might look like this:
$ mkdir build
$ cd build
$ cmake -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DCMAKE_LIBRARY_PATH=/usr/local/cuda/orin/lib64/ -DCMAKE_INCLUDE_PATH=/usr/local/cuda/orin/include -DBUILD_TEGRA=True ..
Note that in the current branch sample cross-compilation for QNX is not fully validated. This placeholder will be updated in the near future with QNX cross-compilation instructions. In the meantime, if you want to cross-compile for QNX please check out one of the previous tags prior to the CMake build system transition in 12.8.
Basic CUDA samples for beginners that illustrate key concepts with using CUDA and CUDA runtime APIs.
Utility samples that demonstrate how to query device capabilities and measure GPU/CPU bandwidth.
Samples that demonstrate CUDA related concepts and common problem solving techniques.
Samples that demonstrate CUDA Features (Cooperative Groups, CUDA Dynamic Parallelism, CUDA Graphs etc).
Samples that demonstrate how to use CUDA platform libraries (NPP, NVJPEG, NVGRAPH cuBLAS, cuFFT, cuSPARSE, cuSOLVER and cuRAND).
Samples that are specific to domain (Graphics, Finance, Image Processing).
Samples that demonstrate performance optimization.
Samples that demonstrate the use of libNVVVM and NVVM IR.
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 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 its README's Dependencies section.
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 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.
To set up FreeImage on a Windows system, extract the FreeImage DLL distribution into the folder ../../../Common/FreeImage/Dist/x64 such that it contains the .h and .lib files. Copy the .dll file to the Release/ Debug/ execution folder or pass the FreeImage folder when cmake configuring with the -DFREEIMAGE_INCLUDE_DIR and -DFREEIMAGE_LIBRARY options.
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. On Windows, to build and run MPI-CUDA applications one can install MS-MPI SDK.
Some samples can only be run on a 64-bit operating system.
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 Microsoft Visual Studio 2012 or higher which provides Microsoft Windows SDK for Windows 8.
DirectX 12 is a collection of advanced low-level programming APIs which can reduce driver overhead, designed to allow development of multimedia applications on Microsoft platforms starting with Windows 10 OS onwards. For Microsoft platforms, NVIDIA's CUDA Driver supports DirectX. Few CUDA Samples for Windows demonstrates CUDA-DirectX12 Interoperability, for building such samples one needs to install Windows 10 SDK or higher, with VS 2015 or VS 2017.
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 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.
Vulkan is a low-overhead, cross-platform 3D graphics and compute API. Vulkan targets high-performance realtime 3D graphics applications such as video games and interactive media across all platforms. On systems which support Vulkan, NVIDIA's Vulkan implementation is provided with the CUDA Driver. For building and running Vulkan applications one needs to install the Vulkan SDK.
GLFW is a lightweight, open-source library designed for managing OpenGL, OpenGL ES, and Vulkan contexts. It simplifies the process of creating and managing windows, handling user input (keyboard, mouse, and joystick), and working with multiple monitors in a cross-platform manner.
To set up GLFW on a Windows system, Download the pre-built binaries from GLFW website and extract the zip file into the folder, pass the GLFW include header as -DGLFW_INCLUDE_DIR for cmake configuring and follow the Build_instructions.txt in the sample folder to set up the t.
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 is a windowing system found on the QNX operating system. Screen is usually found as part of the root filesystem.
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.
EGL is an interface between Khronos rendering APIs (such as OpenGL, OpenGL ES or OpenVG) and the underlying native platform windowing system.
EGLOutput is a set of EGL extensions which allow EGL to render directly to the display.
EGLSync is a set of EGL extensions which provides sync objects that are synchronization primitive, representing events whose completion can be tested or waited upon.
NvSci is a set of communication interface libraries out of which CUDA interops with NvSciBuf and NvSciSync. NvSciBuf allows applications to allocate and exchange buffers in memory. NvSciSync allows applications to manage synchronization objects which coordinate when sequences of operations begin and end.
NvMedia provides powerful processing of multimedia data for true hardware acceleration across NVIDIA Tegra devices. Applications leverage the NvMedia Application Programming Interface (API) to process the image and video data.
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 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.
CDP (CUDA Dynamic Parallellism) 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.
Multi Block Cooperative Groups(MBCG) extends Cooperative Groups and the CUDA programming model to express inter-thread-block synchronization. MBCG is available on GPUs with Pascal and higher architecture.
Multi Device Cooperative Groups extends Cooperative Groups and the CUDA programming model enabling thread blocks executing on multiple GPUs to cooperate and synchronize as they execute. This feature is available on GPUs with Pascal and higher architecture.
CUBLAS (CUDA Basic Linear Algebra Subroutines) is a GPU-accelerated version of the BLAS library.
IPC (Interprocess Communication) allows processes to share device pointers.
CUFFT (CUDA Fast Fourier Transform) is a GPU-accelerated FFT library.
CURAND (CUDA Random Number Generation) is a GPU-accelerated RNG library.
CUSPARSE (CUDA Sparse Matrix) provides linear algebra subroutines used for sparse matrix calculations.
CUSOLVER library is a high-level package based on the CUBLAS and CUSPARSE libraries. It combines three separate libraries under a single umbrella, each of which can be used independently or in concert with other toolkit libraries. The intent ofCUSOLVER is to provide useful LAPACK-like features, such as common matrix factorization and triangular solve routines for dense matrices, a sparse least-squares solver and an eigenvalue solver. In addition cuSolver provides a new refactorization library useful for solving sequences of matrices with a shared sparsity pattern.
NPP (NVIDIA Performance Primitives) provides GPU-accelerated image, video, and signal processing functions.
NVGRAPH is a GPU-accelerated graph analytics library.
NVJPEG library provides high-performance, GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications.
NVRTC (CUDA RunTime Compilation) is a runtime compilation library for CUDA C++.
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.
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.
FP16 is a 16-bit floating-point format. One bit is used for the sign, five bits for the exponent, and ten bits for the mantissa.
NVCC support of C++11 features.
The libNVVM samples are built using CMake 3.10 or later.
We welcome your input on issues and suggestions for samples. At this time we are not accepting contributions from the public, check back here as we evolve our contribution model.
We use Google C++ Style Guide for all the sources https://google.github.io/styleguide/cppguide.html
Answers to frequently asked questions about CUDA can be found at http://developer.nvidia.com/cuda-faq and in the CUDA Toolkit Release Notes.
- Teapot image is obtained from Wikimedia and is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license. The image is modified for samples use cases.
