NVIDIA CUDA Code Samples

The CUDA Toolkit includes 100+ code samples, utilities, whitepapers, and additional documentation to help you get started developing, porting, and optimizing your applications for the CUDA architecture. You can get quick access to many of the toolkit resources on this page, CUDA Documentation, or download the complete toolkit.

Please note that you may need to install the latest NVIDIA drivers and CUDA Toolkit to compile and run the code samples.

Refer to the samples CUDA Samples Release Notes for more information.


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.		Browse Files


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.		Browse Files


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.		Browse Files


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.		Browse Files


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.		Browse Files


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.		Whitepaper Browse Files


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.		Browse Files


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.		Browse Files


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		Browse Files


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.		Browse Files


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.		Browse Files


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.		or later Browse Files


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		Browse Files


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.		Browse Files


MersenneTwisterGP11213 This sample demonstrates the Mersenne Twister random number generator GP11213 in cuRAND.		or later Browse Files


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 .		Browse Files


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)		or later Browse Files


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


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


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


Optical Flow Variational optical flow estimation example. Uses textures for image operations. Shows how simple PDE solver can be accelerated with CUDA.		or later Whitepaper Browse Files


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.		Browse Files


Using Inline PTX A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel.		or later Browse Files


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


Image Segmentation using Graphcuts with NPP This sample that demonstrates how to perform image segmentation using the NPP GraphCut function.		or later Browse Files


Histogram Equalization with NPP This SDK sample demonstrates how to use NPP for histogram equalization for image data.		or later Browse Files


FreeImage and NPP Interopability A simple SDK sample demonstrate how to use FreeImage library with NPP.		or later Browse Files


Box Filter with NPP A NPP SDK sample that demonstrates how to use NPP FilterBox function to perform a Box Filter.		or later Browse Files


Preconditioned Conjugate Gradient This sample implements a preconditioned conjugate gradient solver on GPU using CUBLAS and CUSPARSE library.		or later Browse Files


Random Fog This sample illustrates pseudo- and quasi- random numbers produced by CURAND.		or later Browse Files


Monte Carlo Single Asian Option This sample uses Monte Carlo to simulate Single Asian Options using the NVIDIA CURAND library.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


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


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.		or later Browse Files


simplePrintf This CUDA Runtime API sample is a very basic sample that implements how to use the printf function in the device code. Specifically, for devices with compute capability less than 2.0, the function cuPrintf is called; otherwise, printf can be used directly.		or later Browse Files


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 neibors. 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"		or later Browse Files


ConjugateGradient This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library.		or later Browse Files


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


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


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


Simple Multi Copy and Compute Supported in GPUs with Compute Capability 1.1, overlaping 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.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


Device Query This sample enumerates the properties of the CUDA devices present in the system.		or later Browse Files


Device Query Driver API This sample enumerates the properties of the CUDA devices present using CUDA Driver API calls		or later Browse Files


batchCUBLAS A SDK sample that demonstrates how using batched CUBLAS API calls to improve overall performance.		or later Browse Files


Template using CUDA Runtime A trivial template project that can be used as a starting point to create new CUDA Runtime API projects.		or later Browse Files


Template A trivial template project that can be used as a starting point to create new CUDA projects.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


asyncAPI This sample uses CUDA streams and events to overlap execution on CPU and GPU.		or later Browse Files


Clock This example shows how to use the clock function to measure the performance of kernel accurately.		or later Browse Files


Simple Atomic Intrinsics A simple demonstration of global memory atomic instructions. Requires Compute Capability 1.1 or higher.		or later Browse Files


Pitch Linear Texture Use of Pitch Linear Textures		or later Browse Files


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 1.1 or higher.		or later Browse Files


Simple Templates This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays.		or later Browse Files


CUDA C 3D FDTD This sample applies a finite differences time domain progression stencil on a 3D surface.		or later Browse Files


Simple Texture Simple example that demonstrates use of Textures in CUDA.		or later Browse Files


Simple Texture (Driver Version) Simple example that demonstrates use of Textures in CUDA. This sample uses the new CUDA 4.0 kernel launch Driver API.		or later Browse Files


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.		or later Browse Files


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).		Whitepaper Browse Files


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


Simple CUBLAS Example of using CUBLAS using the new CUBLAS API interface available in CUDA 4.0.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


Simple Texture 3D Simple example that demonstrates use of 3D Textures in CUDA.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


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.		or later Browse Files


Scalar Product This sample calculates scalar products of a given set of input vector pairs.		or later Browse Files


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		or later Browse Files


Aligned Types A simple test, showing huge access speed gap between aligned and misaligned structures.		or later Browse Files


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 CUDA runtime Runtime and CUDA Driver API calls.		or later Browse Files


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.		or later Whitepaper Browse Files


1D Discrete Haar Wavelet Decomposition Discrete Haar wavelet decomposition for 1D signals with a length which is a power of 2.		or later Browse Files


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.		or later Whitepaper Browse Files


Fast Walsh Transform Naturally(Hadamard)-ordered Fast Walsh Tranform for batched vectors of arbitrary eligible(power of two) lengths		or later Browse Files


CUDA Histogram This sample demonstrates efficient implementation of 64-bin and 256-bin histogram.		or later Whitepaper Browse Files


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/).		or later Browse Files


Matrix Transpose This sample demonstrates Matrix Transpose. Different performance are shown to achieve high performance.		or later Whitepaper Browse Files


Box Filter Fast image box filter using CUDA with OpenGL rendering.		or later Browse Files


Post-Process in OpenGL This sample shows how to post-process an image rendered in OpenGL using CUDA.		or later Browse Files


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.		or later Whitepaper Browse Files


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.		or later Browse Files


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.		or later Whitepaper Browse Files


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.		or later Whitepaper Browse Files


Sobel Filter This sample implements the Sobel edge detection filter for 8-bit monochrome images.		or later Browse Files


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.		or later Browse Files


Bicubic Texture Filtering This sample demonstrates how to efficiently implement bicubic Texture filtering in CUDA.		or later Browse Files


Fluids (OpenGL Version) An example of fluid simulation using CUDA and CUFFT, with OpenGL rendering.		or later Whitepaper Browse Files


CUDA FFT Ocean Simulation This sample simulates an Ocean heightfield using CUFFT and renders the result using OpenGL.		or later Browse Files


FFT-Based 2D Convolution This sample demonstrates how 2D convolutions with very large kernel sizes can be efficiently implemented using FFT transformations.		or later Browse Files


CUDA Separable Convolution This sample implements a separable convolution filter of a 2D signal with a gaussian kernel.		or later Whitepaper Browse Files


Texture-based Separable Convolution Texture-based implementation of a separable 2D convolution with a gaussian kernel. Used for performance comparison against convolutionSeparable.		or later Browse Files


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).		or later Browse Files


CUDA Radix Sort using the 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.		or later Whitepaper Browse Files


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 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		or later Browse Files


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		or later Browse Files


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.		or later Whitepaper Browse Files


Black-Scholes Option Pricing This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula.		or later Whitepaper Browse Files


Niederreiter Quasirandom Sequence Generator This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution function for Standart Normal Distribution generation.		or later Browse Files


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		or later Whitepaper Browse Files


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!		or later Browse Files


Particles This sample uses CUDA to simulate and visualize a large set of particles and their physical interaction. Adding "-particles=" 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		or later Whitepaper Browse Files


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.		or later Browse Files


Volume Rendering with 3D Textures This sample demonstrates basic volume rendering using 3D Textures.		or later Browse Files


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". 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=" to the command line will allow users to set # of bodies for simulation. Adding “-numdevices=” 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.		or later Whitepaper Browse Files


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.		or later Whitepaper Browse Files


Sobol Quasirandom Number Generator This sample implements Sobol Quasirandom Sequence Generator.		or later Browse Files


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.		or later Browse Files