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World’s Largest Cerebras Processor Is Hundreds of Times Faster than NVIDIA GPU-Based Supercomputer
nineteen.eleven.2021 [12:23],
Gennady Detinich

In the era of the completion of Moore’s law, the famous joke about the world’s largest Soviet microcircuits is no longer a joke. Cerebras WSE processor with 21.5 × 21.5 cm crystal sides has become a new miracle of microelectronics. The first practice of using servers based on Cerebras WSE showed that in a number of tasks it provides overwhelming superiority, and will not have equal for a long time.

Cerebras WSE processor

CS-1 systems based on the Cerebras WSE (Wafer Scale Engine) processor — compact units with a height of 26 inches (66 cm) — were introduced about a year ago. These blocks take up 1/3 of a standard server rack, but their performance is colossal. Moreover, all this performance is the result of the work of a single processor. SeaMicro’s designers took the risk of developing a processor the size of an entire silicon wafer and did not lose. The output turned out to be a product that, in computational tasks of 10 thousand. times faster than the highest performing GPUs.

Recently, practical tests of CS-1 were reported by the US Department of Energy’s National Energy Technology Laboratory (NETL). She became one of the few owners of the CS-1 system and was able to compare it with classic supercomputers based on x86 processors with NVIDIA GPUs. In particular, CS-1 was compared with one of the latest Joule supercomputers obtained by the laboratory.

CS-1 system. Image source: LLNL

The Joule system is ranked 82nd on the list of the 500 most powerful systems on Earth. She relies on 84 thousand. computing cores distributed over dozens of racks is estimated at tens of millions of US dollars and consumes 450 kW. Cerebras CS-1 system takes up a third of the rack, costs several million dollars and consumes only 20 kW. At the same time, in a task that loads 16 384 Joule cores, the system on Cerebras turned out to be 200 times faster.

It should be said that Cerebras is not suitable for all computing workloads. Designed with a focus on accelerating neural network computations, this processor is ideal for AI and machine learning. But even in this case, he needs a powerful external server for the preparation and subsequent processing of the received data. «Raw» data does not suit him, or they do not allow loading such a monster in an optimal way.

And yet for modeling, for example, hydrodynamic problems, he has no equal, which was shown by comparison with the Joule system, which is precisely designed to solve similar problems. Moreover, Cerebras CS-1 is so fast that it can solve complex problems faster than simulated processes occur in real time. For example, he finishes calculating a nuclear reaction long before it stops in a real reactor. And instead of months of training for a particular ML model, the CS-1 system is able to learn in a matter of minutes.

You can read more about the Cerebras processor in our news archive. Let us briefly recall that it consists of 1.2 trillion transistors, some of which are configured in 400 thousand. computing cores.

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