The National Nuclear Security Administration’s (NNSA’s) Lawrence Livermore National Laboratory (LLNL) has broken ground on its Exascale Computing Facility Modernization (ECFM) project. It will substantially upgrade the mechanical and electrical capabilities of the Livermore Computing Center. The upgrades will enable the facility to provide exascale-class service (supercomputers capable of at least one quintillion calculations per second) to the NNSA laboratories: LLNL, Los Alamos and Sandia.
Amid the COVID-19 pandemic, Lawrence Livermore National Laboratory (LLNL) and its industry partners are committed to applying the nation’s most powerful supercomputers and knowledge in computational modeling and data science to fighting the deadly disease.
To assist in this effort, LLNL, Penguin Computing and AMD have reached an agreement to upgrade the lab’s unclassified, Penguin Computing-built Corona high performance computing (HPC) cluster with an in-kind contribution of cutting-edge AMD Instinct accelerators, which is expected to nearly double the peak performance of the machine.
U.S. Secretary of Energy Rick Perry today announced a request for proposals 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. Among other benefits, the systems will help nuclear security, a major piece of the nation’s critical infrastructure.
Researchers at the National Institute of Standards and Technology (NIST) have built a superconducting switch that learns like a biological system and could connect processors and store memories in future computers, NIST officials intend to announce today. The switch in some ways outperforms the human brain that inspired it and offers a wide range of benefits for medical diagnoses, smart cars and intelligence analysis.
The NIST switch is called a synapse, after its biological counterpart, and it supplies a missing piece for neuromorphic computers. Envisioned as a new type of artificial intelligence, such computers could boost machine perception and decision making.
The U.S. Air Force Research Laboratory (AFRL) and IBM are collaborating on a brain-inspired supercomputing system powered by a 64-chip array. The laboratory is investigating applications for the system in embedded, mobile, autonomous settings where limiting factors today include size, weight and power.
As an end-to-end software ecosystem, the scalable platform would enable deep neural-network learning and information discovery. Its advanced pattern recognition and sensory processing power would be the equivalent of 64 million neurons and 16 billion synapses; however, the processor component only will consume approximately 10 watts, the equivalent of a dim light bulb.
Researchers are developing an open source machine-learning framework that allows a distributed network of computers to process vast amounts of data as efficiently and effectively as supercomputers and to better predict behaviors or relationships. The technology has a broad range of potential applications, including commercial, medical and military uses.
Anyone who needs to analyze a few trillion datasets can use a supercomputer or distribute the problem among processors on a large network. The former option is not widely available, and the latter can be complicated.
Researchers for the U.S. intelligence community intend to build software applications that will make it easier to design and develop superconducting networks to power future supercomputers capable of much faster processing with lower energy requirements. The tools will reduce the time and cost to design superconductor-based circuits, potentially revolutionizing the computer and electronics industry.