Aug. 23--A machine dubbed "Cyence," the most powerful computer on the Iowa State University campus, is capable of more than 183 trillion calculation per seconds.
The computer, with total memory of 38.4 trillion bytes, is just beginning to produce data for 17 research projects, but university officials say the thinking and infrastructure behind the new machine will have much broader effects across the university, according to a news release issued by ISU.
"The whole campus is excited about this, and so am I," said Arun Somani, an Anson Marston distinguished professor in electrical and computer engineering and associate dean for research in the College of Engineering. "We expect to expand our science research with the help of high performance computing. We also expect this will expand Iowa State research. If you don't have a machine powerful enough to do the calculations, you can't even propose a project."
Cyence, with its 12 black cabinets and rows of blue lights, is front and center in the Machine Room in the basement of the Durham Center. It has been running since early July. It succeeds Cystorm and its 28.16 trillion calculations per second as the most powerful computer on campus.
It was purchased with the help of a three-year, $1.8 million major research instrumentation grant from the National Science Foundation. Another $800,000 is being provided by Iowa State's Office of the Vice President for Research and Economic Development and the colleges of agricultural and life sciences, engineering and liberal arts and sciences.
Somani led the faculty team that applied for the National Science Foundation grant.
The team proposed that Cyence be used to support 17 research projects from eight Iowa State departments, including work in bioscience, chemistry, ecology, fluid dynamics, atmospheric science, materials science and energy systems.
Somani, for example, will use the new computer to help develop new and better infrastructure designs for the country's energy and transportation systems.
"This computer enables us to solve larger models covering longer time frames," Somani said. "We can do a lot more. This will help us a lot."
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