Industrial Use of TSUBAME2.5
Partnership Resource Allocations

TSUBAME Industrial Use -Statistical InformationTSUBAME Services

TSUBAME is open to academia and industries. Industrial use started in FY2007.
TSUBAME Usage Profile
75.63%

2011

20.74%

72.34%

2012

62.01%

20132014

56.25%

2015

55.40%

2016*

66.30%

23.45%
15.36%22.63%
24.35% 19.40%
24.16% 20.44%
19.21%14.23%

Internal Academia
External Academia
External Industry*: As of 2016/09/30

The Number of Industrial Projects
Trial Use (Fully Subsidized)
5040

-2015

Newly Accepted Continuations

2016-

HPCI (Full Year) HPCI (Half Year)

Pay UseOpenClosed

How to Use TSUBAME?
User types

Non-Tokyo Tech Users

Industrial Users

20All students have TSUBAME
accounts.
Partnership Resource
Allocations

Academic and Industrial Use

HPCI/JHPCN

Academic and Industrial Use
Supported by MEXT

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

International Collaboration

Collaborators with Tokyo
Tech Professors

Research Collaboration
based on Research Fund or
Industrial Contracts

PriceRemarks

Trial Use

OpenFreeSupported byMEXT

Pay UseOpen$0.39/NodeH

Closed

$1.57/NodeHIntellectual properties are reserved completely by theusers and are not required to be shared with Tokyo Tech.
“NodeH” is the unit for pricing. 1 NodeH is equivalent to 1node for 1 hour.

Project for Creation ofIndustrial Use
Research Platforms andSupported by MEXT
Sharing of Advanced
Research Infrastructure (-2015)

Foreign Researchers

Publicity

Exchange rate is calculated with $1 = ¥102.

For example, if you pay $39, you can use 100 nodes for 1 hour,or 1 node for 100 hours.

Each node has 2 Intel Xeon processors (12 cores) and 3
NVIDIA Tesla K20x GPUs, with 56GB Memory.
“Publicity: Open” requires company name, division, purpose to use and the report of result to be published.
“Publicity: Closed” only requires company name to bepublished.

HPCI/JHPCN (2016-)

100Remarks column

Tokyo Tech Students andProfessors(Permanent call for proposals)

30ProgramsMenu

Distributed Computing for Machine Learning on Large-Scale Image Dataset
Ikuro Sato*, Ryutaro Watanabe*, Hiroki Nishimura**, Akihiro Nomura***, Satoshi Matsuoka*** (*DENSO IT LABORATORY, INC., **DENSO CORPORATION, ***Tokyo Institute of Technology)(This article is extracted from TSUBAME e-Science Journal Vol 14.)

Intensive researches have been revealing that machine-learning methods known as
Deep Neural Networks (DNNs) show great classification capabilities through supervised
training on massive datasets. This research aims to quantify a condition that primarily controls classification capabilities, and generate a high-performing ensemble classifier consisting of plural DNN models. As for the training, we used node-distributed machine-learning program that we developed from scratch. As many as 96 GPUs are used to train
a single DNN model. Most models are trained during the TSUBAME Grand
Challenge, using 1146 GPUs simultaneously at peak, reaching about 1 TFLOPS (single)
per GPU in the cost derivative parts.
(a)
(b)
(c)

(a)(b)

Figure Validation images falsely classified by our model.
(a) Arabian camel, (b) triceratops.
60model#sampletop-5 error rate

55
5045
40
35
30
25
20
15
0

Figure Validation images correctly classified by our model.
(a) Unicycle, (b) paddle, (c) bubble.

510 15 20 25 30 35
number of parameters (1.0e6)

40

Figure The relationship between the classification accuracy and the number oftraining parameters. The blue line indicates the number of training samples.

Numerical simulation of air/water multiphase flows for ceramic sanitary ware design by multiple GPUs
Akio Ikebata*, Shinya Yoshida*, Feng Xiao** (*TOTO LTD., **Tokyo Institute of Technology) (This article is extracted from TSUBAME e-Science Journal Vol 8.)

We have been developing an in-house CAE air/water two-phase numerical code for various purposes in design and manufacturing of plumbing
products such as ceramic sanitary wares. In order to re-produce the complex interfacial flows of air and water with adequate accuracy, large scalecomputations are required with reliable numerical model, which is of greatchallenge. To this end, we have made efforts to improve the numerical
schemes and port the code to the GPU platforms to accelerate the large
scale computations for real-case applications. We have implemented
large-scale simulation on the TSUBAME2.0 supercomputer by making effective use of the GPGPU architecture, and achieved significant improvement in both computational performance and simulation results.

Figure Three dimensional parallel partition.
(conceptual rendering)

Figure Speedup of PCG solver by multi-GPUs.

Figure Real case simulation of Sanitary set by TSUBAME2.0 supercomputer.

http://www.gsic.titech.ac.jp/sc16