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09-17-11, 09:00 AM
The parallel computing movement is charging full steam ahead, with over three dozen new universities and research institutions around the world now participating in our CUDA Research Center and CUDA Teaching Center programs.

In our continued push (http://blogs.nvidia.com/2011/07/new-cornell-collaboration-explores-gpu-computing-using-matlab/) to help solve the world‚??s most complex scientific challenges with GPUs, we‚??ve recently added 43 new CUDA Research Centers and CUDA Teaching Centers ‚?? collectively known as ‚??CUDA Centers‚?? ‚?? to our roster of more than 126 existing CUDA Centers worldwide (81 Teaching Centers and 45 Research Centers).

In addition, there are more than 460 universities worldwide that are teaching parallel programming courses based on CUDA Architecture. With support from NVIDIA, the world is set to welcome tens of thousands of graduating students each year, armed with the knowledge and expertise to leverage the parallel processing power of GPUs.

The CUDA Research Center Program targets active research centers looking to use GPUs in math, science and engineering applications. Participating research universities and institutions get access to exclusive networking events and educational seminars featuring key researchers and academics, a designated NVIDIA CUDA technical liaison and specialized online and in-person training sessions.

The CUDA Teaching Center Program is aimed at institutions that are integrating GPU computing techniques into their mainstream computer-programming curriculum. The benefits of becoming a CUDA Teaching Center include: donated teaching kits, textbooks (http://www.elsevierdirect.com/morgan_kaufmann/kirk/), software licenses and NVIDIA CUDA architecture-enabled GPUs (http://www.nvidia.com/object/cuda_gpus.html) for teaching lab computers. NVIDIA also offers academic discounts for additional hardware.

Here are some examples of CUDA-related work taking place at some of these new CUDA Centers:

[/URL]http://blogs.nvidia.com/wp-content/uploads/2011/09/CMU-SV-Logo-Web1.jpg (http://blogs.nvidia.com/wp-content/uploads/2011/09/CMU-SV-Logo-Web.jpg)Carnegie Mellon Silicon Valley‚?? Machine Learning Algorithms
The CUDA Research Center at Carnegie Mellon University‚??s Silicon Valley campus (CMUSV) focuses on application-driven research in computer science and engineering that enhance human communication and improve user safety. This CUDA Research Center addresses the challenges associated with accelerating machine learning algorithms for use in real-world technologies such as speech recognition, computer vision and natural language¬*processing.

http://blogs.nvidia.com/wp-content/uploads/2011/09/ICRAR-Univ-WA-Logo-Web1.jpg

ICRAR ‚?? Radio AstronomyRadio Astronomy is fast becoming a High Performance Computing exercise, and GPUs offer an affordable way to provide that performance. Whether the GPU is used in: the search for new mysterious astronomical objects (such as the strange `Lorimer Burst‚?? discovered in radio observations but not at all understood), for new forms of signals(such as Gravity Waves), or for the processing of the torrent of data generated by the next- generation `Square Kilometre Array‚?? telescope, the collaboration between ICRAR and NVIDIA could make it possible to tackle complex challenges, which would result in a myriad of spin-offs.

University of Edinburgh ‚?? Condensed Matter Physics
http://blogs.nvidia.com/wp-content/uploads/2011/09/Univ-Edinburgh-Logo-Web1.jpgResearchers at EPCC (The University of Edinburgh) are using their expertise in large-scale parallel computing and CUDA to create an application capable of simulating extremely complex fluid dynamics models on the largest of NVIDIA GPU accelerated supercomputers to power cutting-edge research in condensed matter physics. They are also actively involved in the development of new programming techniques which promise to increase productivity, in particular the expansion of the OpenMP directive-based model to support accelerators.

We‚??d like to extend a warm welcome to all our new CUDA Centers:
New CUDA Research Centers



Carnegie Mellon University Silicon Valley
CINECA (Italy)
Czech Technical University in Prague (Czech Republic)
George Mason University
ICRAR (Australia)
Indian Institute of Technology Madras (India)
Institute for System Programming of Russian Academy of Sciences (ISP RAS) (Russia)
Philipps-Universität Marburg (Germany)
Poznan Supercomputing and Networking Center (Poland)
SciNet, University of Toronto (Canada)
The University of Edinburgh (UK)
Universidad Nacional de Córdoba (Argentina)
Universidade Federal do Rio de Janeiro¬* (Brazil)

New CUDA Teaching Centers



[U]Auburn University
Cankaya University (Turkey)
Center for Development of Advanced Computing (CDAC) (India)
George Mason University
Institute of Technology Tallaght (Ireland)
Jaypee University of Information Technology (India)
Lamar University
National University of Córdoba (Argentina)
Nirma University (India)
North Carolina Agricultural and Technical State University
Novosibirsk State University (Russia)
Philipps-Universität Marburg (Germany)
Polytechnic Institute of Leiria (Portugal)
Rochester Institute of Technology
Saint Louis University
Ss Cyril & Methodius University (Macadonia)
The University of West Indies (Trinidad & Tobago)
Turkish Air Force Academy (Turkey)
UFA State Aviation Tech University (Russia)
Universidad de Alicante (Spain)
Universidad T√©cnica Federico Santa Mar√*a (Chile)
Universidade Federal do ABC (Brazil)
Universidade Federal do Rio de Janeiro (Brazil)
Universite d‚??Orleans (France)
University of Belgrade ‚?? Faculty of Mathematics (Serbia)
University of Maryland, Baltimore County
University of Pittsburgh
University of Texas at Austin
University of Toronto (Canada)
Valdosta State University The future looks bright for GPU-based (Graphics Processing Units) parallel computing. For more information on NVIDIA research activities and these CUDA Center programs, please visit the NVIDIA Research site (http://research.nvidia.com/).


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