As artificial intelligence increasingly takes center stage in computer technology, large-scale orders are heading to Japan's supercomputer makers from research entities in need of the massive processing power those machines provide. Fujitsu is assembling a dedicated AI-use supercomputer for the Institute of Physical and Chemical Research, better known as Riken, which plans to start using it in April. Tokyo Institute of Technology, also known as Tokyo Tech, has ordered a large-scale system for AI-education purposes from SGI Japan, another supercomputer maker. The institute plans to introduce it in August. And industry insiders are looking forward to an even bigger opportunity: a huge AI-use supercomputer the National Institute of Advanced Industrial Science and Technology, or AIST, wants to start using in 2018.

Tokyo Tech is in the process of building its next-generation TSUBAME supercomputer, featuring NVIDIA GPU technology and the company's Accelerated Computing Platform. TSUBAME 3.0, as the system will be known, will ultimately be used in tandem with the existing TSUBAME 2.5 system, to deliver an estimated 64.3 (in aggregate) PFLOPS of AI compute horsepower. On its own, TSUBAME 3.0, is expected to offer roughly two times the performance of its predecessor. TSUBAME 3.0 will be built around NVIDIA's Pascal-based Tesla P100 GPUs, which are not only more efficient, but higher-performing than previous-generation Maxwell GPUs in terms of performance per watt and performance per die area. It is estimated that TSUBAME 3.0 will deliver roughly 12.2 petaflops of double precision compute performance, which would place it among the world's 10 fastest systems according to the most recent TOP500 list.

The Tokyo Institute of Technology, also known as Tokyo Tech, has revealed that the TSUBAME 3.0 supercomputer scheduled to be installed this summer will provide 47 half precision (16-bit) petaflops of performance, making it one of the most powerful machines on the planet for artificial intelligence computation. For Tokyo Tech, the use of NVIDIA's latest P100 GPUs is a logical step in TSUBAME's evolution. The original 2006 system used ClearSpeed boards for acceleration, but was upgraded in 2008 with the Tesla S1040 cards. In 2010, TSUBAME 2.0 debuted with the Tesla M2050 modules, while the 2.5 upgrade included both the older S1050 and S1070 parts plus the newer Tesla K20X modules. Bringing the P100 GPUs into the TSUBAME lineage will not only help maintain backward compatibility for the CUDA applications developed on the Tokyo Tech machines for the last nine years, but will also provide an excellent platform for AI/machine learning codes. In a press release from NVIDIA published Thursday, Tokyo Tech's Satoshi Matsuoka, a professor of computer science who is building the system, said, "NVIDIA's broad AI ecosystem, including thousands of deep learning and inference applications, will enable Tokyo Tech to begin training TSUBAME 3.0 immediately to help us more quickly solve some of the world's once unsolvable problems."

The Global Scientific Information and Computing Center at the Tokyo Institute of Technology has been at the forefront of accelerated computing, and well before GPUs came along and made acceleration not only cool but affordable and normal. But its latest system, Tsubame 3.0, being installed later this year, the Japanese supercomputing center is going to lay the hardware foundation for a new kind of HPC application that brings together simulation and modeling and machine learning workloads. The hot new idea in HPC circles is not just being able to run machine learning workloads side by side with simulations, but to use machine learning to further accelerate the simulation, and we have a future feature story underway, based on conversations with researchers at TiTech and at Oak Ridge National Laboratory, where the "Summit" hybrid CPU-GPU system is being built for the US Department of Energy, about this very topic. Suffice it to say, the idea is to integrate machine learning into the simulation, to do some of the computationally intensive stuff in a new way. So, as part of a climate model, you teach the system using machine learning to predict the weather by watching movies of the weather, or in astronomy, you use machine learning to remove the noise from the signal to find the interesting bits of a star field.