Berkeley Lab Researchers Help Establish a Next Generation Data Analysis Center
Contact: Linda Vu, firstname.lastname@example.org, 510-495-2402
April 10, 2010
Researchers in the Lawrence Berkeley National Laboratory's (Berkeley Lab) Computational Research Division (CRD) will receive approximately $1 million over the next four years to help establish a new, state-of-the-art visualization data and analysis center aimed at interpreting the massive amounts of data produced by today's most powerful supercomputers.
Called Remote Data Analysis and Visualization (RDAV), the center will be located at the University of Tennesse's (UT) National Institute for Computational Science. The center will be built by a collaboration of researchers from UT, Berkeley Lab, University of Wisconsin at Madison, University of Illinois' National Center for Supercomputing Applications, and the Oak Ridge National Laboratory.
When completed, RDAV will provide remote visualization and image generation, data and statistical analysis, workflow systems, and a variety of software services, making it a leading visualization and data analysis center. The RDAV center will also feature a support staff of visualization and data analysis experts adept at aiding researchers in interpreting their results and incorporating and developing new capabilities for understanding data.
"Our team of experts has been providing these scientific visualization services to thousands of researchers around the globe for decades, as part of the Department of Energy's NERSC Analytics and CRD visualization programs. Our experience and track record in this space made us the best qualified to lead this part of the RDAV effort," says Wes Bethel, who heads the Berkeley Lab's Scientific Visualization Group in CRD. Bethel will also be coordinating the Berkeley Lab’s contribution to the RDAV center.
“Scientific visualization is about more than just generating pretty graphics. In fact, computer simulations are a vital part of the analytics process which is essential for gaining insight from science experiments,” he adds.
Much of RDAV will rely on a new machine named Nautilus that employs the SGI shared-memory parallel architecture. The machine will feature 1,024 cores, 4,096 gigabytes of memory, and 16 graphics processing units. The new SGI system can independently scale processor count, memory, and I/O to very large levels in a single system running standard Linux. This flexibility will allow the RDAV team to configure a system uniquely capable of analyzing and visualizing petascale data sets. Nautilus will be used for three major tasks: visualizing data results from computer simulations with many complex variables, such as weather or climate modeling; analyzing large amounts of data coming from experimental facilities; and aggregating and interpreting input from a large number of sensors distributed over a wide geographic region.
According to Bethel, the Berkeley Lab team will be responsible for installing remote visualization software infrastructure and high performance visual data exploration and analysis software on Nautilus, as well as apply visualization technology to science user projects that run on the system.
Bethel says that the project will leverage the expertise of Berkeley Lab's Visualization and Analytics team in developing software tools and providing support to DOE supported scientific research. "We are extremely excited to extend this expertise to an even broader scientific community through the RDAV center," he adds.
Over the next four years, UT will receive $10 million in funding National Science Foundation's (NSF) TeraGrid eXtreme Digital Resources for Science and Engineering (XD) to lead the RDAV center's development. TeraGrid XD is the next phase in the NSF's ongoing effort to build a cyberinfrastructure that delivers high-end digital services, providing American researchers and educators with the capability to work with extremely large amounts of digitally represented information.
For more information about computing sciences at Berkeley Lab, please visit: www.lbl.gov/cs