NVIDIA Names Georgia Institute of Technology a CUDA Center of Excellence

Leading University and Research Institution Joins Network of 10 Other Celebrated Institutions Focused on Advancing Parallel Computing

SANTA CLARA, CA - NVIDIA today recognized Georgia Institute of Technology (Georgia Tech) as a CUDA Center of Excellence.

One of the world's premier engineering and science universities, Georgia Tech is engaged in a wide number of research, development and educational activities which leverage GPU Computing.

Jeffrey Vetter, joint professor of the Georgia Tech College of Computing and Group Leader at Oak Ridge National Laboratory, will serve as principal investigator of the CUDA Center of Excellence.

"Georgia Tech has a long history of education and research that depends heavily on the parallel processing capabilities that NVIDIA has introduced with its CUDA architecture," Vetter said. "This award allows us to focus, what is now a large amount of activity across 25 different research groups, under a single center, which will significantly amplify our research capabilities."

Georgia Tech's non-profit research arm, Georgia Tech Research Institute, is also leveraging the capabilities of the GPU in its work with industry and government groups such as the U.S. Defense Department.

"By cross-pollinating ideas and skills, sharing software and hardware facilities, and streamlining interactions with priority access to NVIDIA staff and capabilities, this status will add considerable strength to our research and educational programs," Vetter added.

NVIDIA and Georgia Tech are already collaborating on a number of projects that will help shape the national science infrastructure. The National Science Foundation Track 2D Keeneland Project will initially deploy a significant system of NVIDIA® Tesla™ processors this year, with a larger, petaflop-class system to be in place by 2012. Georgia Tech and Oak Ridge are also collaborating with NVIDIA in the recently announced DARPA Ubiquitous High Performance Computing program, with the goal of designing an energy efficient "petaflop in a cabinet¿ prototype system in 2018.

One example of the work the University is doing in the field of software tools is "Ocelot", a compiler that allows CUDA code to run seamlessly on multi-core CPUs. The compiler will be available and distributed through the CCOE and will help to catalyze research on top of this open source infrastructure.

Georgia Tech joins a select group of 10 other universities and research organizations in the U.S. and abroad, including Harvard University, Cambridge University and the Chinese Academy of Sciences, that are designated as a CUDA Center of Excellence. More than 350 universities worldwide teach the CUDA programming model within their curriculum. 

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.

More information on the Georgia Tech CUDA Center of Excellence can be found here, or visit the NVIDIA CUDA Center of Excellence program page for more information on the program.

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 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 May2, 2010. 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.

(C) 2009 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, and CUDA are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice.

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