Lifeboat Foundation

Researchers at the University of Pennsylvania have developed a new computer chip that uses light instead of electricity. This could improve the training of artificial intelligence (AI) models by improving the speed of data transfer and, more efficiently, reducing the amount of electricity consumed.

Humanity is building the exascale supercomputers today that can carry out a quintillion computations per second. While the scale of the computation may have increased, computing technology is still working on the principles that were first used in the 1960s.

Researchers have been working on developing computing systems based on quantum mechanics, too, but these computers are at least a few years from becoming widely available if not more. The recent explosion of AI models in technology has resulted in a demand for computers that can process large sets of information. The inefficient computing systems, though, result in high consumption of energy.

QuEra is cofident that by 2026 it would have built a commercial quantum computer that can beat supercomputers of today with ease.

Basic biology textbooks will tell you that all life on Earth is built from four types of molecules: proteins, carbohydrates, lipids, and nucleic acids. And each group is vital for every living organism.

But what if humans could actually show that these “molecules of life,” such as amino acids and DNA bases, can be formed naturally in the right environment? Researchers at the University of Florida are using the HiPerGator—the fastest supercomputer in U.S. higher education—to test this experiment.

HiPerGator—with its AI models and vast capacity for graphics processing units, or GPUs (specialized processors designed to accelerate graphics renderings)—is transforming the molecular research game.

Using this technique, even a non-conducting material like glass could be turned into a conductor some day feel researchers.

A collaboration between scientists at the University of California, Irvine (UCI) and Los Alamos National Laboratory (LANL) has developed a method that converts everyday materials into conductors that can be used to build quantum computers, a press release said.

Computing devices that are ubiquitous today are built of silicon, a semiconductor material. Under certain conditions, silicon behaves like a conducting material but has limitations that impact its ability to compute larger numbers. The world’s fastest supercomputers are built by putting together silicon-based components but are touted to be slower than quantum computers.

Continue reading “Tiny ‘bending station’ transforms everyday materials into quantum conductors” »

Simulating KH-, RT-, or RM-driven mixing using direct numerical simulations (DNS) can be prohibitively expensive because all the spatial and temporal scales have to be resolved, making approaches such as Reynolds-averaged Navier–Stokes (RANS) often the more favorable engineering option for applications like ICF. To this day, no DNS has been performed for ICF even on the largest supercomputers, as the resolution requirements are too stringent.⁸ However, RANS approaches also face their own challenges: RANS is based on the Reynolds decomposition of a flow where mean quantities are intended to represent an average over an ensemble of realizations, which is often replaced by a spatial average due to the scarcity of ensemble datasets. Replacing ensemble averages by space averages may be appropriate for flows that are in homogenous-, isotropic-, and fully developed turbulent states in which spatial, temporal, and ensemble averaging are often equivalent. However, most HED hydrodynamic experiments involve transitional periods in which the flow is neither homogeneous nor isotropic nor fully developed but may contain large-scale unsteady dynamics; thus, the equivalency of averaging can no longer be assumed. Yet, RANS models often still require to be initialized in such states of turbulence, and knowing how and when to initialize them in a transitional state is, therefore, challenging and is still poorly understood.

The goal of this paper is to develop a strategy allowing the initialization of a RANS model to describe an unsteady transitional RM-induced flow. We seek to examine how ensemble-averaged quantities evolve during the transition to turbulence based on some of the first ensemble experiments repeated under HED conditions. Our strategy involves using 3D high-resolution implicit large eddy simulations (ILES) to supplement the experiments and both initialize and validate the RANS model. We use the Besnard–Harlow–Rauenzahn (BHR) model,^9–12 specifically designed to predict variable-density turbulent physics involved in flows like RM. Previous studies have considered different ways of initializing the BHR model.

We are witnessing a professional revolution where the boundaries between man and machine slowly fade away, giving rise to innovative collaboration.

Photo by Mateusz Kitka (Pexels)

As Artificial Intelligence (AI) continues to advance by leaps and bounds, it’s impossible to overlook the profound transformations that this technological revolution is imprinting on the professions of the future. A paradigm shift is underway, redefining not only the nature of work but also how we conceptualize collaboration between humans and machines.

As creator of the ETER9 Project ⁽²⁾, I perceive AI not only as a disruptive force but also as a powerful tool to shape a more efficient, innovative, and inclusive future. As we move forward in this new world, it’s crucial for each of us to contribute to building a professional environment that celebrates the interplay between humanity and technology, where the potential of AI is realized for the benefit of all.

In the ETER9 Project, dedicated to exploring the interaction between artificial intelligences and humans, I have gained unique insights into the transformative potential of AI. Reflecting on the future of professions, it’s evident that adaptability and a profound understanding of technological dynamics will be crucial to navigate this new landscape.

Continue reading “The Professions of the Future (1)” »

AI processing can take a huge amount of computing power, but by the looks of this latest joint project from the Jülich Supercomputing Center and French computing provider Eviden, power will not be in short supply.

“But can it run Crysis” is an old gag, but I’m still going to see if I get away with it.

China Telecom claims it has built the country’s first supercomputer constructed entirely with Chinese-made components and technology (via ITHome). Based in Wuhan, the Central Intelligent Computing Center supercomputer is reportedly built for AI and can train large language models (LLM) with trillions of parameters. Although China has built supercomputers with domestic hardware and software before, going entirely domestic is a new milestone for the country’s tech industry.

Exact details on the Central Intelligent Computing Center are scarce. What’s clear so far: The supercomputer is purportedly made with only Chinese parts; it can train AI models with trillions of parameters; and it uses liquid cooling. It’s unclear exactly how much performance the supercomputer has. A five-exaflop figure is mentioned in ITHome’s report, but to our eyes it seems that the publication was talking about the total computational power of China Telecom’s supercomputers, and not just this one.

“The memory requirements for PRIYA simulations are so big you cannot put them on anything other than a supercomputer,” Bird said.

TACC awarded Bird a Leadership Resource Allocation on the Frontera supercomputer. Additionally, analysis computations were performed using the resources of the UC Riverside High-Performance Computer Cluster.

The PRIYA simulations on Frontera are some of the largest cosmological simulations yet made, needing over 100,000 core-hours to simulate a system of 3072³ (about 29 billion) particles in a ‘box’ 120 megaparsecs on edge, or about 3.91 million light-years across. PRIYA simulations consumed over 600,000 node hours on Frontera.