Lifeboat Foundation

The teams pitted IBM’s 127-qubit Eagle chip against supercomputers at Lawrence Berkeley National Lab and Purdue University for increasingly complex tasks. With easier calculations, Eagle matched the supercomputers’ results every time—suggesting that even with noise, the quantum computer could generate accurate responses. But where it shone was in its ability to tolerate scale, returning results that are—in theory—far more accurate than what’s possible today with state-of-the-art silicon computer chips.

At the heart is a post-processing technique that decreases noise. Similar to looking at a large painting, the method ignores each brush stroke. Rather, it focuses on small portions of the painting and captures the general “gist” of the artwork.

Continue reading “An IBM Quantum Computer Beat a Supercomputer in a Benchmark Test” »

Wave-based analog computing has recently emerged as a promising computing paradigm due to its potential for high computational efficiency and minimal crosstalk. Although low-frequency acoustic analog computing systems exist, their bulky size makes it difficult to integrate them into chips that are compatible with complementary metal-oxide semiconductors (CMOS). This research paper addresses this issue by introducing a compact analog computing system (ACS) that leverages the interactions between ultrasonic waves and metasurfaces to solve ordinary and partial differential equations. The results of our wave propagation simulations, conducted using MATLAB, demonstrate the high accuracy of the ACS in solving such differential equations. Our proposed device has the potential to enhance the prospects of wave-based analog computing systems as the supercomputers of tomorrow.

Quantum computers are the next step in computation. These devices can harness the peculiarities of quantum mechanics to dramatically boost the power of computers. Not even the most powerful supercomputer can compete with this approach. But to deliver on that incredible potential, the road ahead remains long.

Still, in the last few years, big steps have been taken, with simple quantum processors coming online. New breakthroughs have shown solutions to the major challenges in the discipline. The road is still long, but now we can see several opportunities along the way. For The Big Questions, IFLScience’s podcast, we spoke to Professor Winfried Hensinger, Professor of Quantum Technology at the University of Sussex and the Chief Scientific Officer for Universal Quantum, about the impact these devices will have.

In this video I discuss New Cerebras Supercomputer with Cerebras’s CEO Andrew Feldman.
Timestamps:
00:00 — Introduction.
02:15 — Why such a HUGE Chip?
02:37 — New AI Supercomputer Explained.
04:06 — Main Architectural Advantage.
05:47 — Software Stack NVIDIA CUDA vs Cerebras.
06:55 — Costs.
07:51 — Key Applications & Customers.
09:48 — Next Generation — WSE3
10:27 — NVIDIA vs Cerebras Comparison.

Mentioned Papers:
Massively scalable stencil algorithm: https://arxiv.org/abs/2204.03775
https://www.cerebras.net/blog/harnessing-the-power-of-sparsi…-ai-models.
https://www.cerebras.net/press-release/cerebras-wafer-scale-…ge-models/
Programming at Scale:
https://8968533.fs1.hubspotusercontent-na1.net/hubfs/8968533…tScale.pdf.
Massively Distributed Finite-Volume Flux Computation: https://arxiv.org/abs/2304.

Continue reading “This New AI Supercomputer Outperforms NVIDIA! (with CEO Andrew Feldman)” »

New Study Solves Mystery on Insulator-to-Metal Transition

A study explored insulator-to-metal transitions, uncovering discrepancies in the traditional Landau-Zener formula and offering new insights into resistive switching. By using computer simulations, the research highlights the quantum mechanics involved and suggests that electronic and thermal switching can arise simultaneously, with potential applications in microelectronics and neuromorphic computing.

Looking only at their subatomic particles, most materials can be placed into one of two categories.

After the U.S. government imposed crippling sanctions against select Chinese high-tech and supercomputer companies through 2019 and 2020, firms like Huawei had to halt chip development; it is impossible to build competitive processors without access to leading-edge nodes. But Jiangnan Computing Lab, which develops Sunway processors, and National Supercomputing Center in Wuxi kept building new supercomputers and recently even submitted results of their latest machine for the Association for Computing Machinery’s Gordon Bell prize.

The new Sunway supercomputer built by the National Supercomputing Center in Wuxi (an entity blacklisted in the U.S.) employs around feature approximately 19.2 million cores across 49,230 nodes, reports Supercomputing.org. To put the number into context, Frontier, the world’s highest-performing supercomputer, uses 9,472 nodes and consumes 21 MW of power. Meanwhile, the National Supercomputing Center in Wuxi does not disclose power consumption of its latest system.

There is increasing talk of quantum computers and how they will allow us to solve problems that traditional computers cannot solve. It’s important to note that quantum computers will not replace traditional computers: they are only intended to solve problems other than those that can be solved with classical mainframe computers and supercomputers. And any problem that is impossible to solve with classical computers will also be impossible with quantum computers. And traditional computers will always be more adept than quantum computers at memory-intensive tasks such as sending and receiving e-mail messages, managing documents and spreadsheets, desktop publishing, and so on.

There is nothing “magic” about quantum computers. Still, the mathematics and physics that govern their operation are more complex and reside in quantum physics.

The idea of quantum physics is still surrounded by an aura of great intellectual distance from the vast majority of us. It is a subject associated with the great minds of the 20^th century such as Karl Heisenberg, Niels Bohr, Max Planck, Wolfgang Pauli, and Erwin Schrodinger, whose famous hypothetical cat experiment was popularized in an episode of the hit TV show ‘The Big Bang Theory’. As for Schrodinger, his observations of the uncertainty principle, serve as a reminder of the enigmatic nature of quantum mechanics. The uncertainty principle holds that the observer determines the characteristics of an examined particle (charge, spin, position) only at the moment of detection. Schrödinger explained this using the theoretical experiment, known as the paradox of Schrödinger’s cat. The experiment’s worth mentioning, as it describes one of the most important aspects of quantum computing.

That’s how Andrew Feldman, CEO of Silicon Valley AI computer maker Cerebras, begins his introduction to his company’s latest achievement: An AI supercomputer capable of 2 billion billion operations per second (2 exaflops). The system, called Condor Galaxy 1, is on track to double in size within 12 weeks. In early 2024, it will be joined by two more systems of double that size. The Silicon Valley company plans to keep adding Condor Galaxy installations next year until it is running a network of nine supercomputers capable of 36 exaflops in total.

If large-language models and other generative AI are eating the world, Cerebras’s plan is to help them digest it. And the Sunnyvale, Calif., company is not alone. Other makers of AI-focused computers are building massive systems around either their own specialized processors or Nvidia’s latest GPU, the H100. While it’s difficult to judge the size and capabilities of most of these systems, Feldman claims Condor Galaxy 1 is already among the largest.

Condor Galaxy 1—assembled and started up in just 10 days—is made up of 32 Cerebras CS-2 computers and is set to expand to 64. The next two systems, to be built in Austin, Texas, and Ashville, N.C., will also house 64 CS-2s each.

A collaboration of nuclear theorists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Argonne National Laboratory, Temple University, Adam Mickiewicz University of Poland, and the University of Bonn, Germany, has used supercomputers to predict the spatial distributions of charges, momentum, and other properties of “up” and “down” quarks within protons. The results, just published in Physical Review D, revealed key differences in the characteristics of the up and down quarks.

“This work is the first to leverage a new theoretical approach to obtain a high-resolution map of quarks within a proton,” said Swagato Mukherjee of Brookhaven Lab’s nuclear theory group and a co-author on the paper. “Our calculations show that the up quark is more symmetrically distributed and spread over a smaller distance than the down quark. These differences imply that up and down quarks may make different contributions to the fundamental properties and structure of the proton, including its internal energy and spin.”

Co-author Martha Constantinou of Temple University noted, “Our calculations provide input for interpreting data from nuclear physics experiments exploring how quarks and the gluons that hold them together are distributed within the proton, giving rise to the proton’s overall properties.”