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Category: supercomputing – Page 88

“I feel the need — the need for speed.”

The tagline from the 1980s movie Top Gun could be seen as the mantra for the high-performance computing system world these days. The next milestone in the endless race to build faster and faster machines has become embodied in standing up the first exascale supercomputer.

Exascale might sound like an alternative universe in a science fiction movie, and judging by all the hype, one could be forgiven for thinking that an exascale supercomputer might be capable of opening up wormholes in the multiverse (if you subscribe to that particular cosmological theory). In reality, exascale computing is at once more prosaic — a really, really fast computer — and packs the potential to change how we simulate, model and predict life, the universe and pretty much everything.

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Computer chips in development at the University of Wisconsin–Madison could make future computers more efficient and powerful by combining tasks usually kept separate by design.

Jing Li, an assistant professor of electrical and computer engineering at UW–Madison, is creating computer chips that can be configured to perform complex calculations and store massive amounts of information within the same integrated unit — and communicate efficiently with other chips. She calls them “liquid silicon.”

“Liquid means software and silicon means hardware. It is a collaborative software/hardware technique,” says Li. “You can have a supercomputer in a box if you want. We want to target a lot of very interesting and data-intensive applications, including facial or voice recognition, natural language processing, and graph analytics.”

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Optalysys’s technology performs a mathematical function called the Fourier transform by encoding data, say a genome sequence, into a laser beam. The data can be manipulated by making light waves in the beam interfere with one another, performing the calculation by exploiting the physics of light, and generating a pattern that encodes the result. The pattern is read by a camera sensor and fed back into a conventional computer’s electronic circuits. The optical approach is faster because it achieves in a single step what would take many operations of an electronic computer.

The technology was enabled by the consumer electronics industry driving down the cost of components called spatial light modulators, which are used to control light inside projectors. The company plans to release its first product next year, aimed at high-performance computers used for processing genomic data. It will take the form of a PCI express card, a standard component used to upgrade PCs or servers usually used for graphics processors. Optalysys is also working on a Pentagon research project investigating technologies that might shrink supercomputers to desktop size, and a European project on improving weather simulations.

In 2015, Optalysis built a prototype that achieves a processing speed equivalent to 320 Gflops and it is incredibly energy efficient as it uses low-powered, cost effective components.

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In Brief:

  • Using an advanced supercomputer, scientists came up with a profile for dark matter, concluding that it may be made of axions of a specific type.
  • With this new information, the race is on to be the first to prove the existence of dark matter particles.

Understanding what dark matter is has proven to be amazingly difficult. Of course, one might expect this from a thing that is, for all intents and purposes, entirely invisible. Scientists have come to the conclusion that dark matter exists by observing the way gravity behaves—either our model of gravity is in need of an update, or dark matter exists. The latter is the most likely conclusion.

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NEWS ANALYSIS: The confluence of big data, massively powerful computing resources and advanced algorithms is bringing new artificial intelligence capabilities to scientific research.

WASHINGTON, DC—Massively parallel supercomputing hardware along with advanced artificial intelligence algorithms are being harnessed to deliver powerful new research tools in science and medicine, according to Dr. France A. Córdova, Director of the National Science Foundation.

Córdova spoke Oct. 26 at GPU Technology Conference organized by Nvidia, a company that got its start making video cards for PCs and gaming systems, that now manufactures advanced graphics processor for high-performance servers and supercomputers.

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When we type in a search query, access our email via the cloud or stream a viral video, chances are we don’t spend any time thinking about the technological plumbing that is behind that instant gratification.

Sitaram Lanka and Derek Chiou are two exceptions. They are engineers who spend their days thinking about ever-better and faster ways to get you all that information with the tap of a finger, as you’ve come to expect.

Now, they have a new superpower to help them out.

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New method for information storage via QC uncovered.

Abstract: Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have made a discovery that could lay the foundation for quantum superconducting devices. Their breakthrough solves one the main challenges to quantum computing: how to transmit spin information through superconducting materials.

Every electronic device — from a supercomputer to a dishwasher — works by controlling the flow of charged electrons. But electrons can carry so much more information than just charge; electrons also spin, like a gyroscope on axis.

Harnessing electron spin is really exciting for quantum information processing because not only can an electron spin up or down — one or zero — but it can also spin any direction between the two poles. Because it follows the rules of quantum mechanics, an electron can occupy all of those positions at once. Imagine the power of a computer that could calculate all of those positions simultaneously.

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Eurolab HPC tries to assess the future disruptive technology for high performance computing beyond Exascale computers.

They survey the currents state of research and development and its potential for the future of the following hardware technologies:

CMOS scaling
Die stacking and 3D chip technologies
Non-volatile Memory (NVM) technologies
Photonics
Resistive Computing
Neuromorphic Computing
Quantum Computing
Nanotubes
Graphene and
Diamond Transistors

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