NVIDIA Names Stanford University a CUDA Center of Excellence

Tuesday, July 5, 2011

World-Renowned University Joins Elite Network of Celebrated Institutions Focused on Groundbreaking Parallel Computing Education and Research

SANTA CLARA, CA -- NVIDIA today named Stanford University as a CUDA Center of Excellence, honoring the institution's pioneering work in parallel computing research using NVIDIA® CUDA® technology and NVIDIA GPUs.

The Institute for Computational & Mathematical Engineering (ICME) at the Stanford School of Engineering will spearhead the university's CUDA Center of Excellence program in partnership with a number of other departments, including the Department of Computer Science (CS), the Center for Computational Earth and Environmental Sciences (CEES) and the Department of Mechanical Engineering, Flow Physics Division.

The CUDA Center of Excellence program recognizes, rewards and fosters collaboration with leading institutions at the forefront of parallel computing research. A world leader in computational mathematics, scientific computing and computer science, Stanford joins a network of 11 elite institutions worldwide that have demonstrated a unique vision for improving the technology and application of parallel computing, and are empowering academics and scientists to conduct world-changing research.

Stanford currently offers a number of full courses, short courses and partner-sponsored courses covering CUDA architecture and parallel computing. As a CUDA Center of Excellence, Stanford will utilize GPU computing equipment and grants provided by NVIDIA to support a number of research and academic programs, including:

  • Development of mesh-based solvers for partial differential equations; crucial for simulation of physical phenomena, such as fluid-flow and mechanics
  • Seismic velocity estimation by waveform inversion
  • Probability estimation and uncertainty quantification for large-scale engineering systems: hypersonic vehicles, wind turbines, batteries, green buildings, and financial markets

"It's vitally important that our faculty be at the forefront of computing technology so that we can continue developing state-of-the-art computational algorithms that drive innovation in the sciences and engineering," said Margot Gerritsen, director, Institute for Computational & Mathematical Engineering, and associate professor, Department of Energy Resources Engineering, at Stanford University. "This award allows us to broadly expand parallel computing education and research programs to large numbers of researchers and students from a wide variety of disciplines."

The CUDA Center of Excellence program is competitive and prestigious, and any institution with a demonstrated commitment to both parallel computing research and education may apply for CCOE status.

Other CUDA Centers of Excellence include: Georgia Tech, Harvard University, Institute of Process Engineering at the Chinese Academy of Sciences, National Taiwan University, Tokyo Tech, Tsinghua University (China), University of Cambridge, University of Illinois at Urbana-Champaign, University of Maryland, University of Tennessee, and University of Utah. For more information on the NVIDIA CUDA Center of Excellence program, visit: http://research.nvidia.com/content/cuda-centers-excellence.

CUDA is NVIDIA's parallel computing architecture, which enables dramatic increases in computing performance by harnessing the power of GPUs. NVIDIA CUDA GPUs support all GPU computing programming models, APIs, and languages, including CUDA C/C++/Fortran, OpenCL, DirectCompute, and the recently announced Microsoft C++ AMP.

More than 450 universities and institutions worldwide teach the CUDA programming model within their curriculum. For more information on NVIDIA CUDA technology, visit: www.nvidia.com/CUDA.

About Stanford University and the ICME
Stanford University is internationally renowned for its programs in computational mathematics, scientific computing and computer science. The Institute for Computational & Mathematical Engineering (ICME) at Stanford University is focused on training undergraduate and graduate students and scholars in mathematical modeling, scientific computing and advanced computational algorithms. The institute has made significant contributions in a variety of areas, including: fluid and solid mechanics, computer graphics, reservoir modeling, bio-engineering, uncertainty quantification, stochastics, optimization, and financial mathematics. ICME offers both MS and Ph.D. degrees, and its 140 graduate students are advised by 45 associated faculty, who represent 16 different departments in four schools on campus.

Tags / Keywords:
NVIDIA, CUDA, GPU, GPU computing, supercomputing, parallel computing, GPGPU, high performance computing, Stanford University, ICME, OpenCL, DirectCompute, developers, research, scientific computing

NVIDIA (NASDAQ: NVDA) awakened the world to the power of computer graphics when it invented the GPU in 1999. Since then, it has consistently set new standards in visual computing with breathtaking, interactive graphics available on devices ranging from tablets and mobile phones to notebooks and workstations. NVIDIA's expertise in programmable GPUs has led to breakthroughs in parallel processing which make supercomputing inexpensive and widely accessible. The Company holds more than 1,900 issued patents worldwide, including ones covering designs and insights that are essential to modern computing. For more information, see www.nvidia.com.

Certain statements in this press release including, but not limited to statements as to: the impact and benefits of NVIDIA CUDA architecture and NVIDIA GPUs; and the effects of the company's patents on modern computing 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: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners products; design, manufacturing or software defects; changes in consumer preferences or demands; 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, or SEC, including its Form 10-Q for the fiscal period ended May 1, 2011. Copies of reports filed with the SEC are posted on the company's 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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