The US Department of Energy (DoE) has opened the bidding process for contracts to build the country's second and third exascale super computers, worth a whopping $1.8 billion.
Once built, the exascale machines ought to be able to offer 50 to 100 times the power of the US's fastest current supercomputer and will be housed at the DoE's National Laboratories
However, the contract to build them hasn't been awarded yet, and so the DoE has sent out a request for proposals (RFP) with a timeframe goal of three to five years.
"The new supercomputers funded through this RFP will be follow-on systems to the first US exascale system authorised 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," energy secretary Rick Perry said in a statement.
"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."
One system is planned for deployment at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, and another 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 US industry," Perry added.
"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 US exascale capabilities and help sustain the global leadership position that the US has long enjoyed in the field of high performance computing for science and industry. This position, though, is coming under increasingly intensive challenges from, in particular, China.
It is also hoped by the DoE that the supercomputers will enable breakthroughs in both science and industry through modelling and simulation, high-performance data analysis, and artificial intelligence and machine learning applications.
However, one of the major challenges in building exascale computers is finding ways to reduce their energy needs, the DoE said.
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