Russian Academy of Sciences Expands GPU Supercomputing Infrastructure to Accelerate Scientific Research
Over 30 Member-Institutes Utilizing GPUs to Enable Scientific Breakthroughs in Hundreds of Research Programs
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SANTA CLARA, Calif.— Nov. 7, 2011—NVIDIA today announced that the Russian Academy of Sciences (RAS), a broad network of scientific research institutes across that country, is updating its main supercomputing center with new NVIDIA® Tesla™ GPUs.
The Russian Academy of Sciences is increasing its parallel-computing resources by adding 128 Tesla M2090 GPUs installed in HP ProLiant SL390 G7 servers, to address the explosive growth in the number of scientific applications that use GPUs to drive research projects.
The Russian Academy of Sciences network of member institutes are focused on large numbers of scientific research projects. More than 30 of these institutes currently utilize GPUs for research across a wide range of scientific fields, including hydro-dynamics, geological modeling, genomic analysis, gas dynamics, computational mathematics, molecular dynamics, image processing, computed tomography, electromagnetics, and others.
Among the member institutes that are leveraging GPUs are: Keldysh Institute of Applied Mathematics RAS (www.keldysh.ru), Institute of Mathematics and Mechanics of UB RAS (www.imm.uran.ru) , Institute of Cytology and Genetics of SB RAS (//www.bionet.nsc.ru), Siberian Supercomputing Center based on Institute of Computational Mathematics and Mathematical Geophysics SB RAS (//www2.sscc.ru/), Institute for Image Processing Systems (www.ipsi.smr.ru), and many others.
The Russian Academy of Science’s utilization of NVIDIA Tesla GPUs adds to a rapidly growing list of prominent organizations worldwide that are embracing parallel computing to accelerate scientific research. Tesla GPUs power three of the world’s five fastest supercomputers, as well as the most powerful Russian supercomputer, Lomonosov, at Moscow State University. In addition, seven of Russia’s top 50 supercomputers are GPU-accelerated, which provide the same total computing capability as the remaining 43 supercomputers combined.
“In my research using industrial codes for free surface flows using Navier-Stokes and shallow water equations, I was able to process research data much more efficiently using GPUs. This enabled me to analyze and monitor five times more dam break scenarios and flood regions,” said Evstigneev Nikolay, senior staff scientist at the Laboratory of Chaotic and Nonlinear Dynamics, Institute for System Analysis RAS.
“At the Institute of Applied Physics, we’re using GPUs and CUDA to simulate light propagation in biological objects,” said Mikhail Kirillin, PhD, senior research fellow, Institute of Applied Physics RAS. “GPUs provide significant performance acceleration for the algorithm used to create 3D re-constructions of fluorophore distribution within bio-tissue, which allows us detect tumor localization with a high degree of accuracy.”
“GPU computing has been instrumental in the development of program and algorithmic simulations and borehole geophysical research,” said Vyacheslav Glinskikh, Ph.D. (Geophysics), Head of Laboratory at Trofimuk Institute of Petroleum Geology and Geophysics SB RAS. “Based on our results, we were able to create new automated geophysical data interpretation systems for the oil and gas industry, promising to dramatically improve efficiencies in oil and gas exploration.”
For more information about the Russian Academy of Sciences and its member institutes, please visit: www.ras.ru/en/index.aspx. For more information on NVIDIA Tesla GPUs for high performance computing, please go here. For more information on CUDA, please go here.
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