Over in the NVIDIA developper section a new driver was spotted. As such consider this a beta driver please. We have a discussion thread open here.
New and Improved CUDA Libraries -------------------------------------------------------------------- CUSPARSE, a new library of GPU-accelerated sparse matrix routines for sparse/sparse and dense/sparse operations CURAND, a new library of GPU-accelerated random number generation (RNG) routines, supporting Sobol quasi-random and XORWOW pseudo-random routines for in both host and device code CUFFT performance tuned for radix-3, -5, and -7 transform sizes on Fermi architecture GPUs CUBLAS performance improved 50% to 300% on Fermi architecture GPUs, for matrix multiplication of all datatypes and transpose variations H.264 encode/decode libraries that were previously available in the GPU Computing SDK are now part of the CUDA Toolkit CUDA Driver & CUDA C Runtime Support for new 6GB Quadro and Tesla products Support for debugging GPUs with more than 4GB device memory. Integrated Tesla Compute Cluster (TCC) support in standard Windows driver packages Development Tools
Multi-GPU debugging support for both cuda-gdb and Parallel Nsight Added cuda-memcheck support for Fermi architecture GPUs NVCC support for Intel C Compiler (ICC) v11.1 on 64-bit Linux distros Miscellaneous
Support for malloc() and free() in CUDA C compute kernels NVIDIA System Management Interface (nvidia-smi) support for reporting % GPU busy, and several GPU performance counters New GPU Computing SDK Code Samples
Several code samples demonstrating how to use the new CURAND library, including MonteCarloCURAND, EstimatePiInlineP, EstimatePiInlineQ, EstimatePiP, EstimatePiQ, and SingleAsianOptionP Conjugate Gradient Solver, demonstrating the use of CUBLAS and CUSPARSE together Function Pointers, a sample that shows how to use function pointers to implement the Sobel Edge Detection filter for 8-bit monochrome images Interval Computing, demonstrating the use of interval arithmetic operators using C++ templates and recursion Simple Printf, demonstrating best practices for using both printf and cuprintf in compute kernels Bilateral Filter, an edge-preserving non-linear smoothing filter for image recovery and denoising that is implemented in CUDA C with OpenGL rendering SLI with Direct3D Texture, a simple example demonstrating the use of SLI and Direct3D interoperability with CUDA C Windows developers should be sure to check out the new debugging and profiling features in Parallel Nsight for Visual Studio at www.nvidia.com/ParallelNsight.
Please refer to the Release Notes and Getting Started Guides for more information.
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 CUDA Toolkit 3.2 Readiness Tech Brief for a summary of these changes.
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