Preferred Networks (PFN) is completing a new private supercomputer, MN-2, which the Japanese AI startup expects to have operational in July 2019.

MN-2 is a cutting-edge multi-node GPGPU*3 computing platform using NVIDIA V100 Tensor Core GPUs. Combined with two other PFN private supercomputers — MN-1, in operation since September 2017; and MN-1b, in operation since July 2018 — Mn-2 will provide PFN with total computing resources of about 200 PetaFLOPS. PFN also plans to start operating MN-3, a private supercomputer with PFN’s proprietary deep learning processor MN-Core, in spring 2020.

PFN believes investing in computing resources will help it accelerate practical R&D applications in deep learning technologies and establish a competitive edge in the global development race.

Conceptual image of MN-2

PFN next-generation private supercomputer MN-2 outline

PFN’s private supercomputer MN-2 is equipped with 5,760 of the latest CPU cores and 1,024 NVIDIA V100 Tensor Core GPUs. Built at the Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology, MN-2 can theoretically perform about 128 PetaFLOPS in mixed precision calculations (a method used in deep learning), giving MN-2 more than double the peak performance of MN-1b.

Each MN-2 node has four 100-gigabit Ethernets, in conjunction with the adoption of RoCEv2*4, to interconnect with other GPU nodes. The uniquely tuned interconnect realizes high-speed, multi-node processing. PFN will concurrently self-build software-defined storage*5 with a total capacity of over 10PB and optimize data access in machine learning to speed up the training process.

PFN will utilize the open-source deep learning framework Chainer on MN-2 to accelerate R&D in fields that require massive computing resources such as personal robots, transportation systems, manufacturing, bio/healthcare, sports, and creative industries.

*１: The figure for MN-1 is the total PetaFLOPS in half precision. For MN-1b and MN-2, the figures are PetaFLOPS in mixed precisions. Mixed precisions are the combined use of more than one precision formats of floating-point operations.

“High computational power is one of the major pillars of deep learning R&D. We are confident that the MN-2 with 1,024 NVIDIA V100s will further accelerate our R&D,” says PRN Corporate Officer, VP of Systems Takuya Akiba.

Says NVIDIA Japan Country Manager and Vice President of Corporate Sales Masataka Osaki: “NVIDIA is truly honored that Preferred Networks has chosen NVIDIA Tesla V100 SMX2 for the MN-2, in addition to the currently operating MN-1 and MN-1b, also powered with our cutting-edge GPUs for data centers. We anticipate that the MN-2, accelerated by NVIDIA’s flagship product with high-speed GPU interconnect NVLink, will spur R&D of deep learning technologies and produce world-leading solutions.”

Preferred Networks was founded in 2014 and promotes business utilization of deep learning technology focused on IoT and Edge Heavy Computing in transportation, manufacturing and bio/healthcare. The company developed the open source deep learning framework Chainer and has collaborated with organizations such as Toyota Motor Corporation, Fanuc Corporation and the National Cancer Center of Japan.