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Characteristic Analysis of Applications for Designing a Future HPC System

  • Osamu WatanabeEmail author
  • Takashi Soga
  • Youichi Shimomura
  • Akihiro Musa
Conference paper

Abstract

Computer simulations have become an effective tool for finding solutions to various social and scientific challenges: countermeasures against disasters, environmental issues, industrial competitiveness, and so on. To research future HPC systems for finding such solutions, the Ministry of Education, Culture, Sports, Science and Technology in Japan conducted a research program, “Feasibility Study of Future HPCI Systems”. Tohoku University, the Japan Agency for Marine-Earth Science and Technology, and NEC Corporation participated in the program. An application research group was formed to clarify characteristics of several application programs in the fields of natural disaster mitigation and high productivity engineering for designing a future HPC system by around 2020. In this article, we describe the overview of our research in the program.The application research group investigates characteristics of the application programs; for example, the ratio of memory bandwidth to computational performance (BF), calculation amount, memory capacity, MPI data traffic, and so on. Then, we clarify that the B/Fs of most researched application programs are greater than 2 B/F. Thus, these application programs are memory intensive, and the B/F in future HPC systems will need 2 B/F or greater to preserve the computational performance. Also, this group estimates the performance of the application programs on our designed future HPC system with a high memory bandwidth. Our research shows that our future HPC system has the potential to overcome the challenges of natural disaster mitigation and high productivity engineering.

Keywords

Application Program Memory Bandwidth Target Application Coupling Simulation Scientific Challenge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was done in collaboration with our application research group, and many colleagues contributed to it. We particularly thank Dr. Takane Hori and Dr. Kenichi Itakura of the Japan Agency for Marine-Earth Science and Technology, Associate Professor Ryusuke Egawa of Tohoku University, and Yoko Isobe and Akihiro Yamashita of NEC for valuable discussions on this research. We also thank Takashi Abe and Kenta Yamaguchi of NEC Solution Innovators and Midori Kano of IX Knowledge for their support.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Osamu Watanabe
    • 1
    Email author
  • Takashi Soga
    • 2
  • Youichi Shimomura
    • 3
  • Akihiro Musa
    • 1
  1. 1.1st Government and Public Solutions DivisionNEC CorporationMinato-ku, TokyoJapan
  2. 2.NEC Solution InnovatorsChuo-ku, OsakaJapan
  3. 3.NEC Solution InnovatorsAoba-ku, SendaiJapan

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