Nvidia Ships CUDA Toolkit 3.2
Nvidia, maker of GPU technology and tools for developers, has announced the production release of CUDA Toolkit 3.2, which provides performance increases, new math libraries and advanced cluster management features for developers creating next-generation GPU-accelerated applications.
The CUDA Toolkit includes all the tools, libraries and documentation developers need to build CUDA C/C++ applications, and is the foundation for many other GPU computing language solutions. CUDA is Nvidia's parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU (graphics processing unit).
New features and significant performance enhancements in version 3.2 include up to 300-percent performance improvement in CUDA Basic Linear Algebra Subroutines (BLAS, and CUDA BLAS is known as CUBLAS) library routines, delivering 8 times faster performance than the latest Intel MKL (Math Kernel Library). Also the CUDA Fast Fourier Transform (FFT), known as CUFFT) library optimizations delivering between two to 20 times faster performance than the latest MKL, Nvidia said.
Moreover, Nvidia said CUDA Toolkit 3.2 provides new CURAND library for random number generation at 10-20 times faster than the latest MKL, and new CUSPARSE library of sparse matrix routines that delivers 6-30 times faster performance than the latest MKL. And the new release also boasts a host of additional improvements to GPU debugging and performance analysis tools
In addition, the new CUDA Toolkit 3.2 release includes H.264 encode/decode, new Tesla Compute Cluster (TCC) integration, cluster management features, and support for the new 6GB Nvidia Tesla and Quadro GPU products.
Nvidia is hosting a webinar on Tuesday, Nov. 23 at 10:00 a.m. PT to review the new performance enhancements and capabilities of the new CUDA Toolkit. To register for the webinar, go to: https://www2.gotomeeting.com/register/887428835. For more release highlights and the latest CUDA Toolkit downloads, please visit: www.nvidia.com/getcuda.