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Lawrence Livermore National Laboratory has long been one of the world’s largest consumers of supercomputing capacity. With computing power of more than 200 petaflops, or 200 billion floating-point operations per second, the U.S. Department of Energy-operated institution runs supercomputers from every major U.S. manufacturer.

For the past two years, that lineup has included two newcomers: Cerebras Systems Inc. and SambaNova Systems Inc. The two startups, which have collectively raised more than $1.8 billion in funding, are attempting to upend a market that has been dominated so far by off-the-shelf x86 central processing units and graphics processing units with hardware that’s purpose-built for use in artificial intelligence model development and inference processing to run those models.

Cerebras says its WSE-2 chip, built on a wafer-scale architecture, can bring 2.6 trillion transistors and 850,000 CPU cores to bear on the task of training neural networks. That’s about 500 times as many transistors and 100 times as many cores as are found on a high-end GPU. With 40 gigabytes of onboard memory and the ability to access up to 2.4 petabytes of external memory, the company claims, the architecture can process AI models that are too massive to be practical on GPU-based machines. The company has raised $720 million on a $4 billion valuation.

Chip startup Cerebras Systems Inc. today debuted Andromeda, a supercomputer optimized to run artificial intelligence applications that features more than 13.5 million processor cores.

Sunnyvale, California-based Cerebras Systems is backed by more than $720 million in venture funding. The startup sells a chip called the WSE-2 that is specifically designed to run AI software. The new Andromeda supercomputer that Cerebras Systems debuted today is based on the WSE-2 chip.

According to Cerebras Systems, Andromeda can provide performance in excess of one exaflop when running AI applications. One exaflop equals 1 million trillion calculations per second. The startup says that Andromeda’s performance makes it suitable for, among other use cases, training large language models, which are complex neural networks that can perform tasks such as translating text and generating software code.

The U.S. Department of Energy’s INCITE program has awarded supercomputing time to 56 projects!

Managed jointly by the Argonne Leadership Computing Facility and Oak Ridge Leadership Computing Facility, INCITE supports large-scale, computationally intensive projects that address “grand challenges” in science and engineering.

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The U.S. Department of Energy’s (DOE) Office of Science announced allocations of supercomputer access to 56 high-impact computational science projects for 2023 through its Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. These awards, which will pursue transformational advances in science and engineering, account for 60% of the available time on the leadership-class supercomputers at DOEs Argonne and Oak Ridge national laboratories.

The projects will support a broad range of large-scale research campaigns to advance knowledge in areas ranging from astrophysics to sustainable energy technologies to materials design and discovery.

Jointly managed by the Argonne Leadership Computing Facility (ALCF) and the Oak Ridge Leadership Computing Facility (OLCF), the INCITE program is the primary means by which the facilities fulfill their mission to advance open science by providing the scientific community with access to their powerful supercomputing resources. The ALCF and OLCF are DOE Office of Science user facilities.

The hundreds of gold-rich stars discovered in our Milky Way galaxy may have come from smaller galaxies that merged 10 billion years ago, according to new simulations by a supercomputer.

Using the ATERUI II supercomputer in the Center for Computational Astrophysics at the National Astronomical Observatory of Japan, scientists at Tohoku University and the University of Notre Dame developed new simulations of galaxy formation with the highest resolution yet.

The paper was published this week in the Monthly Notices of the Royal Astronomical Society.

Could energy efficiency be quantum computers’ greatest strength yet?

Bartlomiej Wroblewski/iStock.

Continue reading “Quantum computers’ secret power: How they could dramatically boost energy efficiency” »

IBM plans to move into a more modular design for future quantum computers to allow for more flexibility and rapid scale-up of qubits.

With mathematical modeling, a research team has now succeeded in better understanding how the optimal working state of the human brain, called criticality, is achieved. Their results mean an important step toward biologically-inspired information processing and new, highly efficient computer technologies and have been published in Scientific Reports.

“In particular tasks, supercomputers are better than humans, for example in the field of artificial intelligence. But they can’t manage the variety of tasks in everyday life —driving a car first, then making music and telling a story at a get-together in the evening,” explains Hermann Kohlstedt, professor of nanoelectronics. Moreover, today’s computers and smartphones still consume an enormous amount of energy.

“These are no sustainable technologies—while our brain consumes just 25 watts in everyday life,” Kohlstedt continues. The aim of their interdisciplinary research network, “Neurotronics: Bio-inspired Information Pathways,” is therefore to develop new electronic components for more energy-efficient computer architectures. For this purpose, the alliance of engineering, life and natural sciences investigates how the human brain is working and how that has developed.