GeForce GPU Runs Folding@home Protein Simulations 140 Times Faster Than Traditional Processors
"The impact of GeForce GPUs on protein folding simulations was immediate and dramatic," said Vijay Pande, associate professor of chemistry,
The Folding@home project has amassed a large following of computer enthusiasts who compete in teams to churn through as many data units as possible. Their unofficial stats are organized by and displayed at ExtremeOverclocking.com. It took the NVIDIA internal folding team only two weeks to move ahead of 90% of all teams, using only 10 machines. After expanding the team to include more GPUs, the NVIDIA team has moved inside the top 0.1% of teams in all-time total production in less than a month.
Other folding teams are also seeing their status rise as a result of the NVIDIA Folding@home client.
"We saw the completed work double for our PC Games Hardware Folding team as a result of many team members installing the NVIDIA Folding client," said Carsten Spille, editor at PC Games Hardware. "We are passing many teams every day and we have finally reached our goal of being one of the top 100 folding teams in the world."
Proteins assemble themselves through a process biologists call "folding." The goal of the Folding@home project is to understand protein folding, misfolding, and related diseases. Folding@home simulates protein folding in order to understand how proteins fold so quickly and reliably and to learn about what happens when proteins do not fold correctly. Diseases such as Alzheimer's disease, cystic fibrosis, BSE (Mad Cow disease), an inherited form of emphysema, and many cancers are believed to result from protein misfolding.
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Certain statements in this press release including, but not limited to, statements as to: the benefits, features, performance and capabilities of NVIDIA GeForce GPUs and the NVIDIA Folding client; the benefits and impact of Folding@Home program; and the non-gaming applications for GPUs, 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: our reliance on third parties to manufacture, assemble and test our products; development of faster or more efficient GPU or CPU technology; unexpected loss of performance of our products when integrated into systems; the impact of technological development and competition; design, manufacturing or software defects; changes in industry standards and interfaces 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 April 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.
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