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A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This approach bypasses the short timesteps caused by supernova explosions using a surrogate model, thereby improving scalability. With this method, we reached 300 billion particles using 148,900 nodes, equivalent to 7,147,200 CPU cores, breaking through the billion-particle barrier currently faced by state-of-the-art simulations. This resolution allows us to perform the first star-by-star galaxy simulation, which resolves individual stars in the Milky Way Galaxy. The performance scales over $10^4$ CPU cores, an upper limit in the current state-of-the-art simulations using both A64FX and X86-64 processors and NVIDIA CUDA GPUs.

Once confined to specialized workloads with high compute requirements, such as academic and medical research, financial modeling, and energy exploration, high-performance computing, or HPC, has in recent years been finding its way into IT of all stripes. This has partly been brought about by the mainstreaming of machine learning (a subset of artificial intelligence), which generally operates at a snail's pace on conventional servers and needs the added oomph that HPC brings to the table. Like much in life, its boundaries aren't clearly defined and its part of a continuum. Although the term is often used interchangeably with supercomputers, behemoth systems such as Fugaku -- which employs close to 160,000 processors to produce 415.53 petaflops -- HPC systems range from clusters of garden-variety racked x86 servers and storage devices to supercomputers like Fugaku. They're much faster than typical servers, generally employing much more silicon than conventional systems, both as CPUs and GPUs, the latter being used to "accelerate" the system, or offload some of the number crunching from the former.

Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.

Instead, it was born with an "application-first philosophy," meaning that its exclusive purpose is to dedicate its computational excellence to tackling some of the world's biggest challenges, such as climate change, says Satoshi Matsuoka, 57, the mastermind behind the project. "Benchmark excellence is not our priority," he said in an interview conducted in fluent, near flawless English. Instead, he said, its success is assessed "based on how much we can accelerate the applications that are important in society." As the director of Riken's Center for Computational Science, Matsuoka and his team have set out nine application areas for Fugaku to work on that are of importance to society, such as medicine, pharmacology, disaster prediction and prevention, environmental sustainability and energy. Matsuoka began leading the team developing the next-generation supercomputer in around 2010, just before its predecessor K computer became the world's fastest supercomputer in the Top500 benchmark by conducting more than 10 quadrillion calculations per second.

Japan's arm-based Fugaku supercomputing system has been acknowledged as the world's most powerful supercomputer. In June 2020, the system earned the top spot in the Top500 ranking of the 500 most powerful commercially available computer systems on the planet, for its performance on a longstanding metric for massive scientific computation. Although modern supercomputing tasks often emphasize somewhat different capabilities, Fugaku also outperforms by other measures as well. This architecture just wins big time," said Torsten Hoefler of the Swiss Federal Institute of Technology (ETH) Zurich. "It is a super-large step." Hoefler shared the 2019 ACM Gordon Bell Prize with an ETH Zurich team for simulations of heat and quantum electronic flow in nanoscale transistors performed in part on the previous Top500 leader, the Summit System at the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL) in Tennessee.

Twice a year, the world's fastest supercomputers take a test to see which is top of class. These hundred-million-dollar machines usually run on hundreds of thousands of processors, occupy warehouse floors, gobble up copious amounts of energy, and crunch numbers at an ungodly pace. All that computing is directed at some of humanity's toughest challenges with the likes of advanced climate modeling or protein simulations to help cure diseases. For the last two years, the US's Summit was the fastest supercomputer on the planet. But this week, a new system took the crown.