TOKYO – June 23, 2020 – Preferred Networks, Inc. (PFN) and Kobe University announced today that MN-3, PFN’s deep learning supercomputer, topped the latest Green500 list of the world’s most energy-efficient supercomputers. MN-3 is powered by MN-Core™, a highly efficient custom processor co-developed by PFN and Kobe University specifically for use in deep learning.

PFN’s MN-3 deep learning supercomputer

MN-3 has achieved an energy efficiency of 21.11 gigaflops-per-watt (Gflops/W) based on the industry’s standard high-performance Linpack (HPL) benchmark, meaning it performed 21.11 billion calculations for every watt of power consumed in one second. The achievement is 15% higher than the previous Green500 record of 18.404 Gflops/W, which was recorded in June 2018. This demonstrates that MN-Core and MN-3 are leading the global competition for energy-efficient supercomputers specialized for deep learning.

MN-3, which is located in Japan Agency for Marine-Earth Science and Technology (JAMSTEC)’s Simulator Building at Yokohama Institute for Earth Sciences, started operation in May 2020. The system used for MN-3’s performance measurement consisted of 40 nodes and 160 MN-Core processors.

• Peak performance (theoretical): 3.92 Pflops
• Speed of solving linear simultaneous equations (HPL Benchmark): 1.62 Pflops
• Performance for every watt of power consumed: 21.11 Gflops/W

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

Note: The TOP500 entry states that MN-3 has 2,080 cores. This number consists of 160 MN-Core processors, counted as one core each, and 1,920 Intel Xeon processors. MN-Core performs most of the computations for the HPL benchmark measurement.

The key elements that contributed to the achievement are as follows.

1. MN-Core
MN-Core, developed by PFN and Kobe University with support from RIKEN AICS/R-CCS, is equipped with highly efficient compute units designed specifically for deep learning.

MN-Core board

2. MN-Core DirectConnect
The MN-Core DirectConnect interconnect facilitates high-speed, high-efficiency data transmission between the nodes.

3. Optimization techniques for efficient workload management
The performance optimizations used in the HPL Benchmark can also be used to speed up deep learning workloads.

4. High compute-density and locality
The energy efficiency was maximized by densely integrating multiple MN-Core dies onto each board.

The technologies that drastically reduced the environmental impact and operation costs are expected to become a foundation for highly efficient information systems in general as well as supercomputers of the next generation.

PFN plans to further increase MN-3’s energy efficiency by improving the installation methods, cooling and MN-Core-specific middleware.

For more information about PFN’s supercomputers, visit: https://projects.preferred.jp/supercomputers/en/