NVIDIA Achieves Monumental Folding@Home Milestone With Cuda
GPU Computing Delivers Over a Petaflop to put NVIDIA in Top Spot in Distributed Computing ApplicationFor further information, contact:
FOR IMMEDIATE RELEASE
SANTA CLARA, CA—AUGUST 26, 2008—NVIDIA GPUs are contributing over 1 petaflop of processing power to Stanford University’s Folding@home distributed computing application as of last week, according to the statistics published by Stanford. Active NVIDIA® GPUs deliver over 1.25 petaflops, or 42% of the total processing power of the application which seeks to understand how proteins affect the human body.
NVIDIA’s petaflop contribution, nearly half of the processing power on Folding@home, is delivered by just 11,370 of the total active processors used in the project. In comparison, 208,268 CPUs running Windows were active, contributing just 198 teraflops – just 6% of the total processing power in the project.
Stanford University released a Folding@home client specifically for NVIDIA GPUs in June, so this staggering advance has been achieved in only a few months. Developed using NVIDIA CUDA™, a C language programming environment for many-core parallel architectures, the CUDA port of the Folding@home client has delivered more processing power than any other architecture in the history of the project.
“As these statistics show, the impact of NVIDIA GPUs on protein folding simulations has been extraordinary,” said Vijay Pande, associate professor of chemistry, Stanford University and director of the Folding@home project. “Teams that are folding with NVIDIA GPUs are seeing huge boosts to their production and this is helping to accelerate the project significantly.”
“Applications like Folding@home are just the beginning, every day we are seeing more and more examples of computing problems that are benefitting from CUDA and our GPU technologies,” said Michael Steele, general manager of visual consumer solutions at NVIDIA. “I know everyone at NVIDIA has been closely tracking the progress of the Folding@home project since the release of the CUDA port for our GPUs and we are delighted to see them making such a significant and meaningful contribution to what is extremely valuable work.”
Stanford University’s distributed computing program Folding@home has become a major force in researching cures to life-threatening diseases such as cancer, cystic fibrosis, and Parkinson’s disease by combining the computing horsepower of millions of processors to simulate protein folding. The Folding@home project is the latest example in the expanding list of non-gaming applications for graphics processing units (GPU). By running the Folding@home client on an NVIDIA GPUs, protein-folding simulations can be done 140 times faster than on some of today’s traditional CPUs.
Full table of statistics below:
Source: http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats, as of August 19, 2008.
Certain statements in this press release including, but not limited to, statements as to: the benefits, features, impact, and capabilities of NVIDIA GPUs and NVIDIA CUDA technology; the Folding@home project; future uses of CUDA technology and non-gaming applications for the GPU 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 faster or more efficient GPUs by our competitors; use of the CPU rather than the GPU for computing applications; development of more effective or efficient technology to address computing problems; the impact of technological development and competition; design, manufacturing or software defects; changes in end-users' preferences and demands; 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 July 27, 2008. 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.
 A flop is a floating point operation per second, a standard measure of processing power. A teraflop is 1,000 billion flops and a petaflop is 1,000 trillion flops.
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