NVIDIA CUDA Technology Dramatically Advances The Pace Of Scientific Research
Distributed Computing Applications use NVIDIA GPUs for Biomedical Research, Space Exploration and Searching for Extra Terrestrial IntelligenceFor further information, contact:
FOR IMMEDIATE RELEASE:
SANTA CLARA, CA—December 17, 2008— Once thought of as a technology used only for computer games, NVIDIA® GeForce® graphics processing units (GPUs) with CUDA™ technology are now being used for the serious business of scientific computation. Berkeley’s Open Infrastructure for Network Computing (BOINC), one of the leading distributed computing platforms in the world, is using CUDA technology to tap the massively parallel processing power of NVIDIA GPUs with astounding results that could change the pace of scientific discovery through projects like GPUGRID and Einstein@home. The latest breakthrough came with the release of an optimized client that will allow SETI@home to analyze SETI (Search for Extraterrestrial Intelligence) data in about one-tenth of the time it previously took using CPUsi.
“NVIDIA CUDA technology opens up processing power for scientific research that was previously unavailable and impossible for researchers to afford,” said Dr. David Anderson, Research Scientist U.C. Berkeley Space Sciences Laboratory and founder of BOINC. “CUDA technology makes it easy for scientists and researchers to optimize BOINC projects for NVIDIA GPUs and they are already using it for applications in molecular dynamics, protein structure prediction, climate and weather modeling, medical imaging, and many other areas.”
BOINC is a unique approach to supercomputing in which multiple consumer computers are joined together over the Internet and their combined computing power is used to tackle very large computational tasks. BOINC provides the distributed computing grid layer for a wide variety of scientific projects that work to help cure diseases, study global warming, discover pulsars, and do many other types of scientific research on home PCs.
"The molecular simulations performed by our volunteer computing project are some of the most common types performed by scientists, but they are also some of the most computationally demanding and usually require a supercomputer," stated Dr. Gianni De Fabritiis, researcher at the Research Unit on Biomedical Informatics at the Municipal Institute of Medical Research and Pompeu Fabra University in Barcelona. "Running GPUGRID on NVIDIA GPUs innovates volunteer computing by delivering supercomputing class applications on a cost effective infrastructure which will greatly impact the way biomedical research is performed."
“We expect that porting Einstein@Home to GPUs will increase the throughput of our computing by an order of magnitude,” said Bruce Allen, director of the Max Plank Institute for Gravitational Physics and Einstein@Home Leader for the LIGO Scientific Collaboration. “This would permit deeper and more sensitive searches for continuous-wave sources of gravitational waves.”
“Parallel processing is the key to enabling visual computing, whether in the home, office or research lab, and the CUDA-accelerated GPU is the leading engine behind this trend. Distributed computing is an ideal application for parallel processing, so it’s no surprise that these amazing applications are taking advantage of the GPU’s unprecedented computational power” said Michael Steele, General Manager of Visual Consumer Solutions at NVIDIA. “NVIDIA GPUs are transforming the way we work, play, live and discover.”
To download the NVIDIA SETI@home client visit //setiathome.berkeley.edu/cuda.php. For more information on BOINC visit //boinc.berkeley.edu/. For more information on the Einstein@Home visit //einstein.phys.uwm.edu. For more information on GPUGRID visit //www.gpugrid.net/.
Certain statements in this press release including, but not limited to, statements as to the benefits, impact, performance, power and capabilities of NVIDIA GeForce GPUs with CUDA technology; the partnership between NVIDIA and BOINC and its projects; and the impact of BOINC, SETI@home, GPUGRID and Einstein@Home in their respective fields of study 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 technology; the impact of technological development and competition; 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 including its Form 10-Q for the fiscal period ended October 26, 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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iiBased on a consistent and reproducible SETI@home workload. Time-to-compute is measured and lower time is better. NVIDIA® GeForce® GTX 280-based system processes workload on the NVIDIA GPU and is based on an NVIDIA nForce® 780i SLI™-based motherboard, NVIDIA GTX 280 GPU, Intel Core i7 965 CPU, 2GB DDR2 DRAM and processes the workload in 391 seconds. “Fastest consumer multicore CPU-based system” processes the entire workload on CPU and is based on an ATI Radeon HD4870 GPU, Intel x58-based motherboard, Intel Core i7 965, 3GB DDR3 DRAM and processes the workload in 670 seconds. “Average dual core CPU-based system” processes the entire workload on CPU and is based on an ATI Radeon HD4870 GPU, AMD Phenom 9950 CPU (Dual Core 2.66GHz) 2GB DDR2 DRAM and processes the workload in 3,777 seconds
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Note to editors: If you are interested in viewing additional information on NVIDIA, please visit the NVIDIA Press Room at http://www.nvidia.com/page/press_room.html