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Optimized COAWST for the Vector Supercomputer SX-ACE

In: Sustained Simulation Performance 2018 and 2019

Author

Listed:
  • Shivanshu Kumar Singh

    (NEC Technologies India)

  • Kota Sakakura

    (NEC Corporation)

  • Sourav Saha

    (NEC Technologies India)

  • Koji Goto

    (NEC Corporation)

  • Raghunandan Mathur

    (NEC Technologies India)

  • Osamu Watanabe

    (NEC Corporation)

  • Akihiro Musa

    (NEC Corporation)

Abstract

Tropical cyclones cause immense damage with destructive winds, storm surges, and heavy rainfall with flooding in coastal regions. The intensity and frequency of tropical cyclones are expected to increase as a result of climate change. Therefore, the impact of high-intensity tropical cyclones, i.e., supertyphoons under the climate change needs to be researched. Our goal is to study the characteristics of supertyphoons under different conditions using a scientific application, named Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), and to optimize COAWST to predict the probable damage with proper warning and adequate accuracy on the NEC SX-ACE vector supercomputer. COAWST was developed by the US Geological Survey (USGS) to understand coastal changes caused by natural processes, and SX-ACE is a modern supercomputers with powerful vector cores, and is widely used to solve the large scale issues related to climatology and meteorology. In this paper, we proposed some vectorization strategies on SX-ACE that improve the computational performance of COAWST. Our proposed vectorization strategies have improved performance of COAWST up to 62.7% as compared to its original version for simulation. This paper aims to showcase the importance of the vectorization technology in order to speedily and accurately simulate supertyphoons related to coastal disasters.

Suggested Citation

  • Shivanshu Kumar Singh & Kota Sakakura & Sourav Saha & Koji Goto & Raghunandan Mathur & Osamu Watanabe & Akihiro Musa, 2020. "Optimized COAWST for the Vector Supercomputer SX-ACE," Springer Books, in: Michael M. Resch & Yevgeniya Kovalenko & Wolfgang Bez & Erich Focht & Hiroaki Kobayashi (ed.), Sustained Simulation Performance 2018 and 2019, pages 79-91, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-39181-2_8
    DOI: 10.1007/978-3-030-39181-2_8
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