Russian Academy of Sciences Expands GPU Supercomputing Infrastructure to Accelerate Scientific Research

Monday, November 7, 2011

Over 30 Member Institutes Utilizing GPUs to Enable Scientific Breakthroughs in Hundreds of Research Programs

SANTA CLARA, CA -- 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 (, Institute for Mathematics and Mechanics of UB RAS (, Institute for Cytology and Genetics of SB RAS (, Siberian Supercomputing Center based on Institute of Computational Mathematics and Mathematical Geophysics SB RAS (, Institute for Image Processing Systems (, and many others.

The Russian Academy of Sciences' 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: For more information on NVIDIA Tesla GPUs for high performance computing, please go here. For more information on CUDA, please go here.

NVIDIA (NASDAQ: NVDA) awakened the world to computer graphics when it invented the GPU in 1999. Today, its processors power a broad range of products from smart phones to supercomputers. NVIDIA's mobile processors are used in cell phones, tablets and auto infotainment systems. PC gamers rely on GPUs to enjoy spectacularly immersive worlds. Professionals use them to create visual effects in movies and design everything from golf clubs to jumbo jets. And researchers utilize GPUs to advance the frontiers of science with high-performance computing. The company holds more than 2,100 patents worldwide, including ones covering ideas essential to modern computing. For more information, see

Certain statements in this press release including, but not limited to statements as to: the effects, benefits and impact of GPUs and parallel computing on scientific research; and the effects of the company's patents on modern computing 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: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners products; 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, or SEC, including its Form 10-Q for the fiscal period ended July 31, 2011. Copies of reports filed with the SEC are posted on the company's 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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NVIDIA (NASDAQ: NVDA) is the AI computing company. Its invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at 
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