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The American supercomputer was the first to overcome the exaflops milestone

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Image source: HPE / Oak Ridge National Laboratory

The TOP500 project has published a new version of the rating of the most productive supercomputers in the world. The first place in it was taken by the American Frontier, built on the platform from HPE Cray and AMD 7A53 Epyc processors. He became the first supercomputer to overcome the exaflops milestone — his performance reaches 1,102 exaflops.

TOP500 releases a rating of the 500 most highly productive supercomputers in the world every six months — in November and June. The performance evaluation in it is formed using the LINPACK benchmark. He evaluates how fast the machine copes with solving a dense system of linear equations — a frequently used operation that gives an approximate idea of performance in real problems. Computers in the ranking are sorted by Rmax — the maximum performance achieved in the benchmark, indicated in flops (the number of floating-point operations per second).

Since June 2020, the first line of the rating has been occupied by the Japanese Fugaku, which, in addition to its power, is notable for being built on processors with ARM architecture. Its maximum performance is 442.01 petaflops. Now the first line is occupied by Frontier, installed at Oak Ridge National Laboratory, whose power reaches 1,101 petaflops or 1,102 exaflops.

Frontier is built on the HPE Cray EX235a platform. It uses AMD 7A53 Epyc processors and AMD Instinct MI250X graphics accelerators. The supercomputer consists of 9408 nodes, each of which has one central processor and four graphics accelerators. In total, it uses 9.2 petabytes of RAM and 753 petabytes of constant memory.

As for Russian computers, there are still seven of them in the rating: Chervonenkis (22nd place), Galushkin (40) and Lyapunov (43) from Yandex, Christofari Neo (46) and Christofari (80) from Sber, Lomonosov-2 (262), owned by MSU, and MTS Thunder (318) from MTS.

In 2018, the editor of N + 1 visited the Govorun supercomputer at JINR and talked with its creators about how it works and what tasks it solves.

Grigory Kopiev

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