11th Symposium

学際大規模情報基盤共同利用・共同研究拠点公募型共同研究 平成31年度採択課題

jh190005-MDH

Ken T. Murata (National Institute of Information and Communications Technology)

Combination of HPC and high-speed data transfer technologies forbig-data processing systems
Overview

Concurrent Processing System

Background
With the rapid growth in the information and communicationtechnologies, large volumes of data from computing resources indatacenters and Internet of Things (IoT) sensors are collected andprocessed in real time. High-speed infrastructure networks play animportant role to support the real-time operations, and a high-speed filetransfer tool is needed to fully utilize the network bandwidth. In
addition, artificial intelligence (AI) enables to improve disastermitigation applications by quickly processing large volumes of complex
and fragmented data.
• Objective
We conduct three case studies to demonstrate large-scale dataprocessing and large-scale information systems. The first is extractionof meteorological information from Himawari-8 satellite data using AI
and visualization on tiled display wall (TDW). The second is concurrent
processing system for high-speed data transfer and visualization ofgenomic information. The third is information extraction of visual IoTdata using deep learning.

• Genomic information collected from Illumina Novaseq 6000 inKyushu University• Data transmission from Kyushu University to Kyoto Universityusing HCP•

Fig. 3 Data transmission from Kyushu University to Kyoto University• Visualization on web in real time

Extraction of Meteorological Information Using AI• Himawari-8 real-time web➢ Provides Himawari-8 satellite sensed images in real time and withfull resolution➢ Mirror sites: Thailand and Philippines

Fig. 4 Example of web application for viewing epigenomic data

Extraction of Visual IoT Data using Deep Learning

Fig. 1 Himawari-8 real-time web (https://himawari.asia/)• Amaterass web➢ Provides ground weather data (e.g. solar radiation) in real time

• SmartSight camera➢ Visual IoT with video transmission system using Raspberry Pi➢ 20 locations:o 12 cameras for weather radar in Kochi prefectureo 2 cameras for aurora observation in Arctic regiono 1 camera for river surveillanceo 1 camera for landslide monitoring in Hirosaki Universityo 10 cameras for observation in Chikuma
o 2 cameras for surveillance in Tokyoo etc.
➢ Video transmission with high resolution (full HD) and high framerate (30 fps)
➢ Data analytics and processing using deep learning with TensorFlow
on supercomputer in Kyoto University➢ Data visualization on TDW in Kyoto University

Fig. 2 Amaterass web (http://amaterass.nict.go.jp/)• Visualization on TDW➢ Data analytics using AI in NICT and Chiba University➢ Data transmission using high performance copy (HCP)➢ Data processing by supercomputer in Tohoku University andNagoya University➢ Data visualization on TDW in Chiba University, RIKEN andKyushu University using STARS by NICT and ChOWDER byKyushu University(a) Visual IoT device(b) Example of real-time monitoring system
Fig. 5 SmartSight camera

Collaborating ResearchersPraphan Pavarangkoon1, Kazunori Yamamoto1, Kazuya Muranaga2, Takamichi Mizuhara3, Ayahiro Takaki3, Eizen Kimura4, Keiichiro Fukazawa5, Ryusuke Egawa6, Takuo Suganuma6,Osamu Tatebe7, Atsushi Higuchi8, Takahiro Katagiri9, Masao Ogino9, Hajime Shimada9, Yasuo Okabe5, Yasuyuki Ohkawa10, Kazumitsu Maehara10, Koji Okamura10, Yoshiaki Kasahara10,Takeshi Nanri10, Kenji Ono10, Tomoki Kimura6, Chihiro Tao1, Fuminori Tsuchiya6, Hajime Kita11, Goh Murakami11, Hideaki Takenaka11, Tomohiro Kawanabe12, Ryuho Kataoka13,and Hiroyuki Kusaka7
1National Institute of Information and Communications Technology; 2Systems Engineering Consultants Co., Ltd.; 3CLEALINKTECHNOLOGY Co., Ltd.; 4National Institute of Public Health,
5Kyoto University; 6Tohoku University; 7University of Tsukuba; 8Chiba University; 9Nagoya University; 10Kyushu University; 11Japan Aerospace Exploration Agency; 12RIKEN;
13National Institute of Polar Research

学際大規模情報基盤共同利用・共同研究拠点 第11回シンポジウム
2019年 7月 11日，12日

THE GRAND HALL （品川）