World's second fastest supercomputer creates the largest simulation of our universe

The world's second fastest supercomputer has created the largest and most complex simulation of the universe in history. Astronomers will use it to test the standard model of cosmology.

The supercomputer stationed at Oak Ridge National Laboratory, known as Frontier, was the fastest until earlier this month, when its rival, El Capitan, located at Lawrence Livermore National Laboratory, came along. Nevertheless, Frontier is a real beast, capable of performing up to 1.1 exaFLOPS, which is equal to 1.1 quintillion operations per second, Space.com writes.

A team from the U.S. Department of Energy's Argonne National Laboratory, led by Salman Habib, director of the Argonne Computational Science Division, used its hardware/hybrid accelerated cosmology code (HACC) on Frontier.

First developed about 15 years ago, HACC models the evolution of the universe. Experts note that its code was written in such a way that it could be adapted to any fastest supercomputer at any given time.

According to scientists, the standard model of cosmology is dominated by two components: dark matter and dark energy. The substance from which humans are made - the so-called baryonic matter - makes up less than 5% of the matter and energy in the universe.

"If we want to know what the universe is up to, we need to simulate both of these things [dark matter and dark energy] … gravity as well as all the other physics including hot gas, and the formation of stars, black holes, and galaxies. These … are what we call cosmological hydrodynamics simulations," said Salman Habib.

Scientists emphasize that the simulations, which will be available to the astronomical community, will allow them to explore their cosmological models by asking questions about the nature of dark matter, the strength of dark energy, or looking for alternative models of gravity, such as Modified Newtonian Dynamics (MOND).

The simulations can then be compared to the results of real astronomical studies to determine which model best fits the observations.

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