The GPU Computing SDK provides examples with source code, utilities, and white papers to help you get started writing GPU Computing software. The full SDK includes dozens of code samples covering a wide range of applications including:
The NVIDIA CUDA Toolkit is required to compile code samples. Please obtain the CUDA Toolkit from CUDA Zone.
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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. |
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Vector Addition Driver API 
This CUDA Driver 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. |
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Device Query 
This sample enumerates the properties of the CUDA devices present in the system. |
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Device Query Driver API 
This sample enumerates the properties of the CUDA devices present using CUDA Driver API calls |
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Template 
A trivial template project that can be used as a starting point to create new CUDA projects. |
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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. |
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Bandwidth Test 
This is a simple test program to measure the memcopy bandwidth of the GPU. It currently 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. |
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asyncAPI 
This sample uses CUDA streams and events to overlap execution on CPU and GPU. |
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Clock 
This example shows how to use the clock function to measure the performance of kernel accurately. |
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Simple Atomic Intrinsics 
A simple demonstration of global memory atomic instructions. Requires Compute Capability 1.1 or higher. |
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Pitch Linear Texture 
Use of Pitch Linear Textures |
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simpleStreams 
This sample uses CUDA streams to overlap kernel executions with memcopies between the device and the host. Requires Compute Capability 1.1 or higher. |
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Simple Templates 
This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays. |
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CUDA C 3D FDTD 
This sample applies a finite differences time domain progression stencil on a 3D surface. |
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Simple Texture 
Simple example that demonstrates use of textures in CUDA. |
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Simple Texture (Driver Version) 
Simple example that demonstrates use of textures in CUDA using the driver API. |
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Simple Vote Intrinsics 
Simple program which demonstrates how to use the Vote (any, all) intrinsic instruction in a CUDA kernel. Requires Compute Capability 1.2 or higher. |
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simpleZeroCopy 
This sample illustrates how to use Zero MemCopy, kernels can read and write directly to pinned system memory. This sample requires GPUs that support this feature (MCP79 and GT200). |
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Matrix Transpose 
Efficient matrix transpose. |
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CUDA Context Thread Management 
Simple program illustrating how to the CUDA Context Management API. CUDA contexts can be created separately and attached independently to different threads. |
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Simple CUBLAS 
Example of using CUBLAS. |
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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. |
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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. |
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Simple Texture 3D 
Simple example that demonstrates use of 3D textures in CUDA. |
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Matrix Multiplication 
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.
CUBLAS provides high-performance matrix multiplication. |
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Matrix Multiplication (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. |
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Scalar Product 
This sample calculates scalar products of a given set of input vector pairs. |
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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. |
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Aligned Types 
A simple test, showing huge access speed gap between aligned and misaligned structures. |
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PTX Just-in-Time compilation 
This sample trates how to use JIT compilation for PTX code. |
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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.
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1D Discrete Haar Wavelet Decomposition 
Discrete Haar wavelet decomposition for 1D signals with a length which is a power of 2. |
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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. |
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Fast Walsh Transform 
Naturally(Hadamard)-ordered Fast Walsh Tranform for batched vectors of arbitrary eligible(power of two) lengths |
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Histogram 
This sample demonstrates efficient implementation of 64-bin and 256-bin histogram.
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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 parallel scan primitive provided by the CUDPP library (http://www.gpgpu.org/developer/cudpp/). |
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New Matrix Transpose 
High Performance matrix transpose. |
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Box Filter 
Fast image box filter using CUDA with OpenGL rendering. |
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Post-Process in OpenGL 
This sample shows how to post-process an image rendered in OpenGL using CUDA. |
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Parallel Reduction 
A parallel sum reduction that computes the sum of large arrays of values. This sample demonstrates several important optimization stratezies for parallel algorithms like reduction. |
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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. |
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Image denoising 
This sample demonstrates two adaptive image denoising technqiues: 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 techique, taking advantage of shared memory, is implemented in addition to DirectX counterparts. |
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Sobel Filter 
This sample implements the Sobel edge detection filter for 8-bit monochrome images. |
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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. |
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CUDA Video Decoder GL API 
This sample demonstrates how to efficiently use the CUDA Video Decoder API to decode MPEG-2 or H.264 sources, perform YUV2RGB convertion of the decoded surface with a CUDA kernel, and output the result to an OpenGL surface. An OpenGL window with current frame and fps is opened, but the decoded video is not displayed on the screen. Requires Compute Capability 1.1 or higher. |
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Bicubic Texture Filtering 
This sample demonstrates how to efficiently implement bicubic texture filtering in CUDA. |
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Fluids (OpenGL Version) 
An example of fluid simulation using CUDA and CUFFT, with OpenGL rendering. |
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FFT Ocean Simulation 
This sample simulates an Ocean heightfield using CUFFT and renders the result using OpenGL. |
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FFT-Based 2D Convolution 
This sample demonstrates how 2D convolutions with very large kernel sizes can be efficiently implemented using FFT transformations. |
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Separable Convolution 
This sample implements a separable convolution filter of a 2D signal with a gaussian kernel. |
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Texture-based Separable Convolution 
Texture-based implementation of a separable 2D convolution with a gaussian kernel. Used for performance comparison against convolutionSeparable. |
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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" SDK sample). Single-pass reduction requires global atomic instructions (Compute Capability 1.1 or later) and the _threadfence() intrinsic (CUDA 2.2 or later). |
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Radix Sort 
This sample demonstrates a very fast and efficient parallel radix sort implemented in C for CUDA. The included RadixSort class can sort either key-value pairs (with float or unsigned integer keys) or keys only. |
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Sorting Networks 
This sample implemenets bitonic sort and odd-even 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
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Binomial Option Pricing 
This sample evaluates fair call price for a given set of European options under binomial model. This sample will also take advantage of double precision if a GTX 200 class GPU is present. |
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Black-Scholes Option Pricing 
This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula. |
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Niederreiter Quasirandom Sequence Generator 
This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution function for Standart Normal Distribution generation. |
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Monte Carlo Option Pricing 
This sample evaluates fair call price for a given set of European options using Monte Carlo approach. This sample use double precision hardware if a GTX 200 class GPU is present. |
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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. |
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MersenneTwister 
This sample implements Mersenne Twister random number generator and Cartesian Box-Muller transformation on the GPU. |
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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 use double precision hardware if a GT200 class GPU is present. Thanks to Mark Granger of NewTek who submitted this sample to the SDK! |
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Particles 
This sample uses CUDA to simulate and visualize a large set of particles and their physical interaction. It implements a uniform grid data structure using either a fast radix sort or atomic operations. |
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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 CUDPP library to perform stream compaction. |
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Volume rendering 
This sample demonstrates basic volume rendering using 3D textures. |
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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". |
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Smoke Particles 
Smoke simulation with volumetric shadows using half-angle slicing technique. Uses CUDA for procedural simulation and sorting and OpenGL for rendering. |
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Sobol Quasirandom Number Generator 
This sample implements Sobol Quasirandom Sequence Generator. |
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Matrix Multiplication (Driver Version) 
This sample implements matrix multiplication using the CUDA 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. |
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simpleMPI 
Simple example demonstrating how to use MPI in combination with CUDA. |
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