How do you build one of the most powerful supercomputing research tools in the country? Here’s one suggestion:
160 square foot monitor with resolution approaching an IMAX theater? Check.
IBM Blue Gene/Q supercomputer with 16,348 processing cores? Check.
Linux supercomputer cluster with 2 petabyte storage capacity? Check.
The University of Rochester has provided a sneak peek of one of its newest – and perhaps coolest – research resources, one that will help scientists better understand and manipulate large and complex sets of data.
The Visualization-Innovation-Science-Technology-Application (VISTA) Collaboratory was built with the support of New York State. The new “lab” is located in the Carlson Science and Engineering Library and is part of the Health Sciences Center for Computational Innovation, a partnership between the University, IBM, and New York State to apply high performance computing to health research.
The new “lab” consists of a 20 food wide and 8 foot high display consisting of 24 monitors, creating an immersive visual experience with 50 megapixels of resolution (4 times the resolution of HD). A display of this scale and resolution allows researchers to observe and compare large sets of data on one screen or study minute details within the context of larger structures, for example the electrical activity of individual cells within a computer model of the entire the human heart.
According to said David Topham, Ph.D., the executive director of the HSCCI and a professor in the Department of Microbiology and Immunology, this capability is critical to unlocking the scientific potential of big data.
“The best analytical tool we have is still the human brain. We can see relationships between data that computers cannot. But in order to do that you have to have the information in front of you so you can see the patterns and connections that matter. In other words, you need to be able to see the forest and the trees simultaneously.”
The new lab will be an integral component of the new University Institute for Data Science.
You can read more about the lab here.
Mark Michaud |
| 0 comments