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With IBM Summit supercomputer, US reclaims top spot from China in high-powered computing

But China has even more systems on the latest Top500 supercomputer list.

After five years playing second fiddle to China, the US once again has bragging rights for the world's fastest supercomputer with a gargantuan IBM machine called Summit at Oak Ridge National Laboratory in Tennessee.

Summit's performance was revealed Monday on the Top500 supercomputer list, which a group of academic researchers updates twice each year, with a speed score of 122.3 quadrillion mathematical operations per second, or 122 petaflops. To match that speed, each person on the planet would have to perform 16 million calculations per second.

The rate of improvement has slowed for the 500 fastest supercomputers in recent years. The three lines, from top to bottom, show the total power of the machines on the Top500 list, the single most powerful model, and the 500th on the list.


The Summit system, with 9,216 IBM processors boosted by 27,648 Nvidia graphics chips, takes as much room as two tennis courts and as much power as a small town. It'll be used for civilian research into subjects like material science, cancer, fusion energy, astrophysics and the Earth's changing climate.

Another system called Sierra, somewhat smaller but built of the same components, will be used for nuclear weapons research at Lawrence Livermore National Laboratory in California. It claimed third place with a speed of 71.6 petaflops, trailing China's Sunway TaihuLight which recorded a 93-petaflop speed.

Some of the racks of computer nodes that make up the Summit supercomputer built by IBM at Oak Ridge National Laboratory in Tennessee.

Some of the racks of computer nodes that make up the Summit supercomputer built by IBM at Oak Ridge National Laboratory in Tennessee.

Genevieve Martin/Oak Ridge National Laboratory

Supercomputers are high-prestige but high-cost systems. The US Energy Department's Coral program to build Summit and Sierra, for example, cost $325 million. But they've played an important role in everything from modeling nuclear weapons without actual explosions to simulating the universe with a method called computational cosmology.

Computational cosmology work lets the SLAC National Accelerator Laboratory near Stanford figure out what makes the universe tick. This video shows galactic collisions that in the real universe take place over hundreds of millions of years.

Video by SLAC/ animation by Stephen Shankland/CNET

Building a better benchmark for supercomputing

There's a national race to a performance threshold called one exaflops -- a quadrillion floating-point mathematical operations per second. IBM thinks it can reach that level, but there's a ways to go yet. For the first time, the total performance of all the 500 systems on the list surpassed one exaflops -- 1.22 exaflops.

But the measurement system the Top500 list uses, a math problem called Linpack, is an incomplete reflection of all the types of work a supercomputer might tackle. On an broader alternative called High Performance Conjugate Gradients (HPCG), Summit and Sierra took the first and second places.

"We have incorporated the HPCG benchmark results into the Top500 list to provide a more balanced look at performance," the organizers said of the June 2018 list. By that measure, Summit scored 2.93 HPCG petaflops and Sierra 1.80 HPCG petaflops.

China has more supercomputers on the list

Although China lost the top spot, it widened its lead as measured by the number of systems on the Top500 list. Compared with the November 2017 list, it advanced from 201 systems to 206. The US dropped from 143 to 124.

China has in recent years surged to become the top purchaser of supercomputers on the Top500 list of the most powerful machines.


Progress in supercomputers has tailed off as chip manufacturers like Intel have struggled to increase the performance of general-purpose processors. As a result, many systems are boosted by specialized chips that can accelerate specific operations. Summit and Sierra use chips that Nvidia spun off from its graphics processor business.

The number of supercomputers using accelerator chips increased from 101 to 110.

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