NVIDIA announced the availability of the Tesla GPU Preconfigured Cluster, an off-the-shelf solution that enables researchers and IT managers to easily add GPU computing capabilities to their existing datacenter systems.

This free webinar series will cover the basics of data parallel computing on GPU’s leveraging NVIDIA’s CUDA architecture. Tutorials will cover many topics including C for CUDA, programming to the OpenCL API, using Direct X Compute and performance optimization techniques, presented by NVIDIA Developer Technology Engineering team.

Join this free AccelerEyes and NVIDIA webinar to learn how to get 10x-50x speed-ups in MATLAB life science applications by using the Jacket CUDA-plugin for GPU computing.

NVIDIA announced that Evolved Machines has activated a 10,800-core parallel computing facility dedicated to the development of artificial neural circuits. The system incorporates 42 NVIDIA Tesla GPU Computing processors with a nominal computation capacity of over 40 teraflops.

NVIDIA announced the CUDA 2.2 Beta, which supports C, OpenCL, DirectX Compute, Fortran and other languages and also offers a slew of new features.

NVIDIA announced that Harvard University has been recognized as a CUDA Center of Excellence, a program rewarding schools that truly embrace the concept of parallel processing as the future of computing.

NVIDIA announced the introduction of NVIDIA SLI Multi-OS, a ground-breaking technology that allows users and visualization applications, for the first time, to take full advantage of multiple NVIDIA Quadro GPUs from a single graphics workstation in a virtualized environment.

NVIDIA took professional graphics to a new level with the launch of its intelligent lineup of Quadro professional GPU solutions. It’s the industry’s most power and advanced line of professional graphics solutions featuring the industry’s first and ONLY 4GB graphics cards, innovative SLI Multi-OS capability and more.