Preferred Networks’ MN-3 Supercomputer Improves Green500 ‘Score’ by 23 Percent

November 16, 2020

TOKYO, Nov. 16, 2020 — Preferred Networks, Inc. (PFN) and Kobe University announced today that MN-3, PFN’s deep learning supercomputer, has achieved an energy efficiency of 26.04 gigaflops-per-watt (Gflops/W), 23.3% above its previous record that topped the Green500 list of the world’s most energy-efficient supercomputers in June 2020. With this new achievement, MN-3 ranked 2nd in the latest Green500 list for November 2020.

Powered by MN-Core, a highly efficient custom processor co-developed by PFN and Kobe University specifically for use in deep learning, MN-3 started operation in May 2020 on a trial basis. Drawing on its software development expertise, PFN continuously improved MN-3’s software stack, especially for command processing and data transfer between memory units, which resulted in a higher computing performance with less nodes and processors than in June 2020.

PFN’s MN-3 deep learning supercomputer

PFN plans to continue developing software for MN-3 to use the supercomputer for its internal research and development purposes, including autonomous driving, robotics and drug discovery.

The comparison of systems used for measurement and their respective performance are as follows.

November 2020

June 2020

Number of nodes

32

40

Number of MN-Core processors

128

160

Number of CPU cores

1,536 Intel Xeon cores

1,920 Intel Xeon cores

Peak performance (theoretical)

3.138 Pflops

3.92 Pflops

HPL Benchmark

1.653 Pflops

1.62 Pflops

Energy efficiency (performance for every watt of power consumed)

26.04 Gflops/W

21.11 Gflops/W

https://www.top500.org/system/179806/

 Note: The TOP500 entry states that MN-3 has 1,664 cores. This number consists of 128 MN-Core processors, counted as one core each, and 1,536 Intel Xeon processors. MN-Core performs most of the computations for the HPL benchmark measurement.

About Preferred Networks

Preferred Networks (PFN) was established in March 2014 with the goal to develop practical, real-world applications of deep learning, robotics and other latest technologies. PFN is currently focused on three priority areas – transportation systems, manufacturing and bio-healthcare – and also exploring the use of deep learning in personal robots, plant optimization, materials discovery, sports analytics and entertainment. In 2015, PFN developed Chainer, the open-source deep learning framework. PFN’s MN-3 supercomputer, which is equipped with the MN-Core processor dedicated for deep learning, topped the Green500 list in June 2020. https://www.preferred.jp/en/


Source: Preferred Networks, Inc. (PFN) and Kobe University

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