JAXA Supercomputer System Annual Report April 2017-March 2018

Report Number: R17ECMP20

Subject Category: Competitive Funding

PDF available here

• Responsible Representative: Takashi Takahashi, Aeronautical Technology Directorate, Numerical Simulation Research Unit
• Contact Information: Yuya Ohmichi ohmichi.yuya@jaxa.jp
• Members: Yuya Ohmichi, Kenji Kobayashi

Abstract

Knowledge extraction techniques for large data sets are important because, recently, the computers and numerical simulation techniques have been highly developed and they produced massive datasets . In this study, we are developing knowledge extraction tools which extract patterns from a large data obtained by unsteady fluid simulations.

Reference URL

N/A

Reasons for using JSS2

Large amount of memory is necessary for performing the developed knowledge extraction tools.

Achievements of the Year

A numerical tool to conducte dynamic mode decomposition and compressed sensing techniques was developed. The tool was applied to a flow field around a re-entry capsule and we found a novel fluid phenomenon that had very long time scale.

Publications

■ Peer-reviewed papers

1)Y. Ohmichi, Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition, AIP Advances, 7(7), 075318, 2017.

Usage of JSS2

Computational Information

• Process Parallelization Methods: N/A
• Thread Parallelization Methods: OpenMP
• Number of Processes: 1
• Elapsed Time per Case: 72.00 hours

Resources Used

Fraction of Usage in Total Resources*1(%): 0.04

Details

Please refer to System Configuration of JSS2 for the system configuration and major specifications of JSS2.

*1: Fraction of Usage in Total Resources: Weighted average of three resource types (Computing, File System, and Archiver).

*2: Fraction of Usage：Percentage of usage relative to each resource used in one year.

JAXA Supercomputer System Annual Report April 2017-March 2018