Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports CUDA Toolkit 10.2.
This section describes the release notes for the CUDA Samples on GitHub only.
- Added
simpleD3D11. Demonstrates CUDA-D3D11 External Resource Interoperability APIs for updating D3D11 buffers from CUDA and synchronization between D3D11 and CUDA with Keyed Mutexes. - Added
simpleDrvRuntime. Demonstrates CUDA Driver and Runtime APIs working together to load fatbinary of a CUDA kernel. - Added
vectorAddMMAP. Demonstrates how cuMemMap API allows the user to specify the physical properties of their memory while retaining the contiguous nature of their access. - Added
memMapIPCDrv. Demonstrates Inter Process Communication using cuMemMap APIs. - Added
cudaNvSci. Demonstrates CUDA-NvSciBuf/NvSciSync Interop. - Added
jacobiCudaGraphs. Demonstrates Instantiated CUDA Graph Update with Jacobi Iterative Method using different approaches. - Added
cuSolverSp_LinearSolver. Demonstrates cuSolverSP's LU, QR and Cholesky factorization. - Added
MersenneTwisterGP11213. Demonstrates the Mersenne Twister random number generator GP11213 in cuRAND.
- Added
vulkanImageCUDA. Demonstrates how to perform Vulkan image - CUDA Interop. - Added
nvJPEG_encoder. Demonstrates encoding of jpeg images using NVJPEG Library. - Added Windows OS support to
nvJPEGsample. - Added
boxFilterNPP. Demonstrates how to use NPP FilterBox function to perform a box filter. - Added
cannyEdgeDetectorNPP. Demonstrates the nppiFilterCannyBorder_8u_C1R Canny Edge Detection image filter function.
- Added
NV12toBGRandResize. Demonstrates how to convert and resize NV12 frames to BGR planars frames using CUDA in batch. - Added
EGLStream_CUDA_Interop. Demonstrates data exchange between CUDA and EGL Streams. - Added
cuSolverDn_LinearSolver. Demonstrates cuSolverDN's LU, QR and Cholesky factorization. - Added support of Visual Studio 2019 to all samples supported on Windows.
- Added
immaTensorCoreGemm. Demonstrates integer GEMM computation using the Warp Matrix Multiply and Accumulate (WMMA) API for integers employing the Tensor Cores. - Added
simpleIPC. Demonstrates Inter Process Communication with one process per GPU for computation. - Added
nvJPEG. Demonstrates single and batched decoding of jpeg images using NVJPEG Library. - Added
bandwidthTest. It measures the memcopy bandwidth of the GPU and memcpy bandwidth across PCI-e. - Added
reduction. Demonstrates several important optimization strategies for Data-Parallel Algorithms like reduction. - Update all the samples to support CUDA 10.1.
- Added
simpleCudaGraphs. Demonstrates CUDA Graphs creation, instantiation and launch using Graphs APIs and Stream Capture APIs. - Added
conjugateGradientCudaGraphs. Demonstrates conjugate gradient solver on GPU using CUBLAS and CUSPARSE library calls captured and called using CUDA Graph APIs. - Added
simpleVulkan. Demonstrates Vulkan - CUDA Interop. - Added
simpleD3D12. Demonstrates DX12 - CUDA Interop. - Added
UnifiedMemoryPerf. Demonstrates performance comparision of various memory types involved in system. - Added
p2pBandwidthLatencyTest. Demonstrates Peer-To-Peer (P2P) data transfers between pairs of GPUs and computes latency and bandwidth. - Added
systemWideAtomics. Demonstrates system wide atomic instructions. - Added
simpleCUBLASXT. Demonstrates CUBLAS-XT library which performs GEMM operations over multiple GPUs. - Added Windows OS support to
conjugateGradientMultiDeviceCGsample. - Removed support of Visual Studio 2010 from all samples.
This is the first release of CUDA Samples on GitHub:
- Added
vectorAdd_nvrtc. Demonstrates runtime compilation library using NVRTC of a simple vectorAdd kernel. - Added
warpAggregatedAtomicsCG. Demonstrates warp aggregated atomics using Cooperative Groups. - Added
deviceQuery. Enumerates the properties of the CUDA devices present in the system. - Added
matrixMul. Demonstrates a matrix multiplication using shared memory through tiled approach. - Added
matrixMulDrv. Demonstrates a matrix multiplication using shared memory through tiled approach, uses CUDA Driver API. - Added
cudaTensorCoreGemm. Demonstrates a GEMM computation using the Warp Matrix Multiply and Accumulate (WMMA) API introduced in CUDA 9, as well as the new Tensor Cores introduced in the Volta chip family. - Added
simpleVoteIntrinsicswhich uses *_sync equivalent of the vote intrinsics _any, _all added since CUDA 9.0. - Added
shfl_scanwhich uses *_sync equivalent of the shfl intrinsics added since CUDA 9.0. - Added
conjugateGradientMultiBlockCG. Demonstrates a conjugate gradient solver on GPU using Multi Block Cooperative Groups. - Added
conjugateGradientMultiDeviceCG. Demonstrates a conjugate gradient solver on multiple GPUs using Multi Device Cooperative Groups, also uses unified memory prefetching and usage hints APIs. - Added
simpleCUBLAS. Demonstrates how perform GEMM operations using CUBLAS library. - Added
simpleCUFFT. Demonstrates how perform FFT operations using CUFFT library.
Download and install the CUDA Toolkit 10.2 for your corresponding platform. For system requirements and installation instructions of cuda toolkit, please refer to the Linux Installation Guide, the Windows Installation Guide, and the Mac 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 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 parent directory of the repo:
Each individual sample has its own set of solution files at:
<CUDA_SAMPLES_REPO>\Samples\<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.
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= - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l, aarch64. By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.
$ make TARGET_ARCH=x86_64
$ make TARGET_ARCH=ppc64le
$ make TARGET_ARCH=armv7l
$ make TARGET_ARCH=aarch64
See here for more details on cross platform compilation of cuda samples. -
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 50 and SM 60, useSMS="50 60".$ make SMS="50 60" -
HOST_COMPILER=<host_compiler> - override the default g++ host compiler. See the Linux Installation Guide for a list of supported host compilers.
$ make HOST_COMPILER=g++
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 50 and SM 60, use SMS="50 60".
$ make SMS="A B ..." -
HOST_COMPILER=<host_compiler> - override the default clang host compiler. See the Mac Installation Guide for a list of supported host compilers.
$ make HOST_COMPILER=clang
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. FreeImage is also redistributed with the CUDA Samples.
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.
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.
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.
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.