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NVIDIA Names University Of Maryland A CUDA Center Of Excellence

 
 

Leading University Joins Prominent Network of Institutions Focused on Parallel Computing

For more information, contact:

Andrew Humber
NVIDIA Corporation
(408) 486 8138
ahumber@nvidia.com

FOR IMMEDIATE RELEASE:

SANTA CLARA, Calif. —Feb. 8, 2010— NVIDIA Corp. announced today that it has recognized the University of Maryland as a CUDA Center of Excellence, placing it in an elite grouping of 9 other universities and research organizations worldwide.

The university was selected for its pioneering use of GPU computing and the CUDA programming model across research and teaching efforts within multiple science and engineering departments.

CUDA™ is NVIDIA’s computing architecture that enables its GPUs to be programmed using industry standard programming languages and APIs, opening up their massive parallel processing power to a broad range of applications beyond graphics.

Other CUDA Centers of Excellence in the U.S. and abroad include Cambridge University, Chinese Academy of Sciences, Harvard University, National Taiwan University, Tokyo Institute of Technology, Tsinghua University, University of Illinois at Urbana-Champaign, University of Tennessee and University of Utah. More than 300 universities worldwide teach the CUDA™ programming model within their curriculum.

“Maryland was one of the first universities to start integrating the use of GPUs and the CUDA architecture into our courses and research,” said Amitabh Varshney, Professor of Computer Science at University of Maryland. “The CUDA programming model is an extremely effective educational tool for students learning parallel programming and no other technology available today provides as powerful and affordable platform for our research as the GPU.”

Researchers at the University of Maryland have been exploring the use of GPUs for general-purpose computing for the past five years, when they have demonstrated how to map a number of problems in science, engineering, and medicine to GPUs. Maryland researchers have also published papers that use the CUDA™ architecture of NVIDIA® GPUs to enable entirely new computational techniques in these disparate fields, ranging from the astrophysical simulation of colliding black holes to the real-time analysis of the acoustic properties of concert halls.

The CUDA Center of Excellence at University of Maryland will support several new projects that make extensive use of GPUs such as DNA sequencing. There has been a dramatic increase in the volume of sequence data that can be analyzed, thanks to GPUs, and sequence alignment programs such as MUMmer, a system developed by University of Maryland with the support of the National Institute of Health, have proven essential to this process. By structuring the required processing in parallel on a GPU, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel. MUMmer GPU is available today through NVIDIA’s Tesla Bio Workbench initiative.

Visit the CUDA Center of Excellence program pages for more information.

About NVIDIA
NVIDIA (Nasdaq: NVDA) awakened the world to the power of computer graphics when it invented the graphics processing unit (GPU) in 1999. Since then, it has consistently set new standards in visual computing with breathtaking, interactive graphics available on devices ranging from portable media players to notebooks to workstations. NVIDIA’s expertise in programmable GPUs has led to breakthroughs in parallel processing which make supercomputing inexpensive and widely accessible. Fortune magazine has ranked NVIDIA #1 in innovation in the semiconductor industry for two years in a row. For more information, see www.nvidia.com.

Certain statements in this press release including, but not limited to, statements as to: the impact and effect of the establishment of the CUDA Center of Excellence; the benefits of NVIDIA’s platforms and technologies; are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: development of more efficient or faster technology; design, manufacturing or software defects; the impact of technological development and competition; changes in consumer preferences and demands; customer adoption of different standards or our competitor's products; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission including its Form 10-Q for the fiscal period ended October 26, 2009. Copies of reports filed with the SEC are posted on our website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

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