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Inside 'El Capitan' the Most Powerful Supercomputer Ever Built. It Will Simulate Nuclear Weapons

The $600-million machine is powered by over 11 million cutting-edge processors.

Tibi Puiu
January 22, 2025 @ 7:00 pm

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Credit: Lawrence Livermore National Laboratory.

Nestled in the Lawrence Livermore National Laboratory (LLNL) in California, this $600 million marvel represents a seismic leap in computing. With its 2.746 exaFLOPS peak performance, “El Capitan” outpaces every other supercomputer on the planet, including the former titleholder, Frontier, at Oak Ridge National Laboratory. For perspective, that’s 2.7 quintillion calculations per second and a million times the power of today’s best smartphones.

El Capitan’s primary mission is critical to U.S. national security: ensuring the safety and reliability of the U.S. nuclear stockpile. Underground testing of nuclear weapons has been banned since 1992. Previously, the U.S. and the Soviet Union agreed to ban testing in the atmosphere, in space, and underwater. This means there’s no practical way to physically test a nuclear weapon. Now, simulations on supercomputers like El Capitan will replace those tests.

“We needed to replace [testing] with simulating on these very large computers,” said Pythagoras Watson, team lead at LLNL. For instance, this machine can predict how weapons age, helping scientists assess if they will still work after decades in storage.

A Giant Among Supercomputers

El Capitan supercomputer during installation
Installing El Capitan. Credit: Lawrence Livermore National Laboratory.

Construction of El Capitan began in May 2023, culminating in a dedication ceremony on January 9, 2025. Patrick Kennedy of ServeTheHome, who attended the unveiling, marveled at the system’s liquid-cooled racks. “It was very quiet on this system, with more noise coming from the storage and other systems on the floor,” he noted.

Powered by over 11 million AMD MI300A processors, El Capitan is a masterpiece of engineering. Each processing unit combines CPUs, GPUs, and memory into a single, liquid-cooled chip, achieving speeds while consuming less energy. “Each rack has 128 compute blades, completely liquid-cooled,” noted Patrick Kennedy from ServeTheHome. This design keeps the system efficient and eerily quiet.

Such specifications place El Capitan leagues ahead of its competitors. It outpaces the second-place Frontier supercomputer by an extraordinary 390 petaflops. To put that into perspective, its sibling system, Tuolumne, which ranks as the world’s tenth-fastest supercomputer, operates at a comparatively modest 208.10 petaflops.

Of course, it would be a huge loss to science for such a powerful computer to only be used for defense purposes. In its “spare time”, the system’s immense computational power will also tackle climate modeling, gene folding, and high-energy physics.

Watson explained the scale with a staggering analogy: “Its peak performance is at 2.79 quintillion calculations per second. That’s more than 70 billion years before the Big Bang,” he told CBS News Bay Area.

El Capitan’s debut underscores a broader trend in computing: the drive toward exascale performance. This shift represents a quintillion calculations per second — a number so vast it can reshape disciplines from medicine to engineering.

But beyond the thousands of chips and miles of cables, the heart of this machine lies in the dedication of scientists and engineers. For Watson, who has spent 24 years at LLNL, El Capitan is more than just a fancy computer. “I really love the fact that this is actually something that really helps the country and the world ultimately,” he said.

Livermore’s Mayor John Marchand, himself a chemist, echoed this pride. “Livermore is only one of six cities in the world to have an element named after us. And to have our name appear in the periodic table of elements… I think that’s really cool.”

Even as El Capitan takes the spotlight, LLNL is already preparing for the future. The lab has outlined plans for its next exascale system, codenamed ATS-6, expected by 2030. The upcoming system will focus on diversifying node types and incorporating AI-optimized hardware to address evolving research needs.

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