Distributed Computing Applications use NVIDIA GPUs for Biomedical Research, Space Exploration and Searching for Extra Terrestrial Intelligence
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"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.
SETI@Home
Researchers in the scientific field of SETI received a massive increase in computing power today, when NVIDIA and BOINC released an optimized client that will allow SETI@home to be accelerated on GeForce GPUs. SETI@home, the largest BOINC project with nearly 200,000 active users, searches for extra terrestrial intelligence by using radio telescopes to listen for narrow-bandwidth radio signals from space. The performance of a GeForce GTX 280 GPU running SETI@ is nearly 2 times faster than the fastest consumer multicore CPU (3.2GHz Intel Core i7 965) and almost 10 times faster than an average dual core consumer CPU (2.66 GHz AMD Phenom 9950)(ii).
GPUGRID
GPUGRID, the first BOINC project to use NVIDIA GeForce GPUs with CUDA technology for computing, utilizes NVIDIA-based graphics cards in participating PCs to compute high-performance biomolecular simulations for scientific research. Adding support for NVIDIA GPUs resulted in 1000 active GPUs delivering the same amount of computing power as 20,000 CPUs in similar projects, delivering an average speed-up of 20 times.
"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."
Einstein@Home
NVIDA CUDA technology will soon be powering the third most widely used BOINC project, Einstein@Home, which uses distributing computing to search for spinning neutron stars (also called pulsars) using data from gravitational wave detectors.
"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 http://setiathome.berkeley.edu/cuda.php. For more information on BOINC visit http://boinc.berkeley.edu/. For more information on the Einstein@Home visit http://einstein.phys.uwm.edu. For more information on GPUGRID visit http://www.gpugrid.net/.
About NVIDIA
NVIDIA (NASDAQ: NVDA) is the world leader in visual computing technologies and the inventor of the GPU, a high-performance processor which generates breathtaking, interactive graphics on workstations, personal computers, game consoles, and mobile devices. NVIDIA serves the entertainment and consumer market with its GeForce graphics products, the professional design and visualization market with its Quadro graphics products, and the high-performance computing market with its Tesla(TM) computing solutions products. NVIDIA is headquartered in Santa Clara, Calif. and has offices throughout Asia, Europe, and the Americas. For more information, visit http://www.nvidia.com.
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.
(C) 2008 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, GeForce, Tesla, and Quadro are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice.
(i) Based on a consistent and reproducible SETI@home workload.
Time-to-compute is measured and lower time is better. NVIDIA(R)
GeForce(R) GTX 280-based system processes workload on the NVIDIA GPU
and is based on an NVIDIA nForce(R) 780i SLI(TM)-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
(ii) Based on a consistent and reproducible SETI@home workload.
Time-to-compute is measured and lower time is better. NVIDIA(R)
GeForce(R) GTX 280-based system processes workload on the NVIDIA GPU
and is based on an NVIDIA nForce(R) 780i SLI(TM)-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
Web site: http://www.nvidia.com/
