Exascale supercomputer will provide 50 to 100 times greater performance than current fastest US supercomputer
US Secretary of Energy Rick Perry announced a Request for Proposals (RFP), potentially worth up to $1.8 billion, for the development of at least two new exascale supercomputers to be deployed at U.S. Department of Energy (DOE) National Laboratories in the 2021-2023 timeframe, according to a press release.
The new supercomputers funded through this RFP will be follow-on systems to the first U.S. exascale system authorized by Secretary Perry this past June, named Aurora, which is currently under development at Argonne National Laboratory (ANL) and scheduled to come online in 2021.
The RFP announced today also envisions the possibility of upgrades or even a follow-on system to Aurora in 2022-2023, depending on an assessment of needs and opportunities at that time.
This RFP calls for a system to be deployed at Oak Ridge National Laboratory in Oak Ridge, Tennessee, and for another to be sited at Lawrence Livermore National Laboratory in Livermore, California.
“These new systems represent the next generation in supercomputing and will be critical tools both for our nation’s scientists and for U.S. industry,” Secretary Perry said.
“They will help ensure America’s continued leadership in the vital area of high performance computing, which is an essential element of our national security, prosperity, and competitiveness as a nation.”
The systems selected from this RFP will build out U.S. exascale capabilities and help sustain the global leadership position that the United States has long enjoyed in the field of high performance computing for science and industry, a position that is under challenge in an increasingly competitive international environment.
The new systems will provide 50 to 100 times greater performance than the current fastest U.S. supercomputer. They will enable breakthroughs in both science and industry through modeling and simulation, high-performance data analysis, and artificial intelligence and machine learning applications.
Potential examples include:
· Identifying next-generation materials
· Deciphering high-energy physics data
· Combating cancer
· Accelerating industrial product design and reducing cost-to-market
· Evaluating options for nuclear security
The RFP, the product of a collaboration among the three laboratories known as CORAL—the Collaboration of Oak Ridge, Argonne and Livermore—provides an opportunity for U.S. industry to develop at least two new unique system designs, each with a possible cost range of $400-$600 million, with a possibility of a third system with a similar or wholly different design from each of the previous two.
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