can be used in image recovery and denoising. for solving sequences of matrices with a shared sparsity pattern. CUDA exports buffers that Vulkan imports as vertex buffer. windowing system. Separable Convolution, Watershed Segmentation NPP, Global Memory to Shared Memory Async Copy, Matrix Multiplication (CUBLAS), Matrix Multiplication (CUDA Runtime API Version), Double Performance has also improved on all Kepler and Fermi GPU architectures as well. A CUDA Sample that demonstrates Cholesky factorization using cuSolverSP's low level APIs. Solver , cuSolverSp Lowlevel QR Solver, cuSolverSp LowlevelCholesky Solver, CUDA Compressible Memory, Memmap IPC Driver API, Vector Addition cuMemMap, Vulkan CUDA Interop PI Approximation, CUDA Tensor Core GEMM, Double Precision Tensor Core GEMM, Matrix Multiplication (CUDA Driver API Version), Tensor Core GEMM A This sample illustrates how to use function pointers and implements the Sobel Edge Detection filter for 8-bit monochrome images.

Example of using CUBLAS using the new CUBLAS API interface available in CUDA 4.0. SM 3.5). are not available on the system, the sample will not be installed. Core GEMM, 1D Discrete Haar Wavelet Decomposition, DCT8x8, Fast Walsh Transform, Stereo Disparity Computation (SAD SIMD Intrinsics), CUDA Context Thread Management, EGLStream_CUDA_CrossGPU, EGLStreams CUDA Interop, EGLSync CUDA Event Interop, Memmap IPC Driver Requires CUBLAS provides high-performance matrix multiplication.

NVIDIA products are not designed, authorized, or warranted to be Samples demonstrating how to use CUDA Graphs API to create, instantiate and launch CUDA Operations. v11.1.1, 2.4. GPU with Compute Capability SM 3.0. is required to run the sample. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation CUFFT Callback Routines are user-supplied kernel routines that CUFFT will call when loading or storing data. the Thrust library. single" arithmetic to improve precision when zooming a long way into the pattern. lists the samples that illustrate how that concept is used. This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. TARGET_ARCH= - cross-compile targeting a specific architecture. Simple example that demonstrates how to use a new CUDA 4.1 feature to support cubemap Textures in CUDA C. A demonstration of CUDA Graphs creation, instantiation and launch using Graphs APIs and Stream Capture APIs. Driver supports DirectX.

completeness of the information contained in this document libNVRTC, CUDA Compressible Memory, Clock libNVRTC, EGLStream_CUDA_CrossGPU, EGLStreams CUDA Interop, EGLSync CUDA Event Interop, Matrix This sample demonstrates C++11 feature support in CUDA. Refer to an excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm. capability 3.5 or higher. NvSciSync allows applications to manage synchronization objects which LD_PRELOAD=/libcuhook.so.1 ./cuHook. Basic Concepts demonstrates how to make use of CUDA features. Direct3D10 Capable device is required. threadFenceReduction, Bindless Texture, CUDA FFT Ocean Simulation, CUDA N-Body Simulation, CUDA N-Body Simulation on Screen, CUDA N-Body Simulation

Simple Layered Texture, Simple Surface Write, Simple Texture, Simple Texture (Driver Version), Texture-based Separable Convolution, ConjugateGradientUM, System wide Atomics, Unified Memory Streams, Unified and other CUDA Memories Performance, conjugateGradient Advanced Key Concepts and Associated Samples, Table 4. Advanced Concepts demonstrate advanced techniques and algorithms implemented with CUDA. A simple example which demonstrates how CUDA Driver and Runtime APIs can work together to load cuda fatbinary of vector add the samples: The Windows samples are built using the Visual Studio IDE.

SM architecture of 3.5 or above.

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