#Nvidia cuda toolkit 3.2 how to
#Nvidia cuda toolkit 3.2 update
#Nvidia cuda toolkit 3.2 driver
#Nvidia cuda toolkit 3.2 windows

With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-acceleratedĮmbedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The NVIDIA® CUDA® Toolkit provides a development environment for creating high performance GPU-acceleratedĪpplications.

#Nvidia cuda toolkit 3.2 update

Incomplete-LU and Cholesky Preconditioned Iterative MethodsĬUDA Toolkit Documentation 12.1 Update 1 ĭevelop, Optimize and Deploy GPU-Accelerated Apps.

Nsight Eclipse Plugins Installation Guide.

NVIDIA Ampere GPU Architecture Tuning Guide.

NVIDIA Ampere GPU Architecture Compatibility Guide.

Please refer to the CUDA Toolkit 3.2 Readiness Tech Brief for a summary of these changes.

#Nvidia cuda toolkit 3.2 driver

In CUDA Toolkit 3.2 and the accompanying release of the CUDA driver, some important changes have been made to the CUDA Driver API to support large memory access for device code and to enable further system calls such as malloc and free. Please refer to the Release Notes and Getting Started Guides for more information.

#Nvidia cuda toolkit 3.2 windows

Windows developers should be sure to check out the new debugging and profiling features in Parallel Nsight v1.5 for Visual Studio. simpleSurfaceWrite, demonstrating how CUDA kernels can write to 2D surfaces on Fermi GPUs Vflocking Direct3D/CUDA, which simulates and visualizes the flocking behavior of birds in flight

#Nvidia cuda toolkit 3.2 how to

cudaEncode, showing how to use the NVIDIA H.264 Encoding Library using YUV frames as input SLI with Direct3D Texture, a simple example demonstrating the use of SLI and Direct3D interoperability with CUDA C Bilateral Filter, an edge-preserving non-linear smoothing filter for image recovery and denoising implemented in CUDA C with OpenGL rendering Simple Printf, demonstrating best practices for using both printf and cuprintf in compute kernels Interval Computing, demonstrating the use of interval arithmetic operators using C++ templates and recursion Function Pointers, a sample that shows how to use function pointers to implement the Sobel Edge Detection filter for 8-bit monochrome images Conjugate Gradient Solver, demonstrating the use of CUBLAS and CUSPARSE in the same application Several code samples demonstrating how to use the new CURAND library, including MonteCarloCURAND, EstimatePiInlineP, EstimatePiInlineQ, EstimatePiP, EstimatePiQ, SingleAsianOptionP, and randomFog New NVIDIA System Management Interface (nvidia-smi) support for reporting % GPU busy, and several GPU performance counters Support for memory management using malloc() and free() in CUDA C compute kernels Support for debugging GPUs with more than 4GB device memory NVCC support for Intel C Compiler (ICC) v11.1 on 64-bit Linux distros Expanded cuda-memcheck support for all Fermi architecture GPUs Multi-GPU debugging support for both cuda-gdb and Parallel Nsight New support for enabling high performance Tesla Compute Cluster (TCC) mode on Tesla GPUs in Windows desktop workstations Support for new 6GB Quadro and Tesla products H.264 encode/decode libraries now included in the CUDA Toolkit New CURAND library of GPU-accelerated random number generation (RNG) routines, supporting Sobol quasi-random and XORWOW pseudo-random routines at 10x to 20x faster than similar routines in MKL

New CUSPARSE library of GPU-accelerated sparse matrix routines for sparse/sparse and dense/sparse operations delivers 5x to 30x faster performance than MKL CUFFT performance tuned for radix-3, -5, and -7 transform sizes on Fermi architecture GPUs, now 2x to 10x faster than MKL CUBLAS performance improved 50% to 300% on Fermi architecture GPUs, for matrix multiplication of all datatypes and transpose variations