Efficacy of a GPGPU-Acceleration to Inundation Flow Simulation in Tonle Sap Lake in Cambodia

Authors

  • Takashi Nakamura Tokyo Institute of Technology
  • Shun Murakami IDAJ Co., LTD., Japan
  • Lun Sambo Institute of Technology of Cambodia
  • Hideto Fujii Yamagata University

DOI:

https://doi.org/10.4186/ej.2019.23.1.151

Abstract

A new 2D numerical model is developed for a rapid computation of the seasonal inundation phenomena in Tonle Sap Lake in Cambodia. In order to overcome a huge computational cost for a prolonged analysis over an extensive area, the General-Purpose computing on Graphics Processing Units (GPGPU) technology is applied to the model. The developed model is applied to a solution of seasonal inundation process for the 154 days in 2002. Calculated result is compared with observational data and satellite remote sensing. It is found that the developed model seems to successfully reproduce reasonable progress/regress of inundation. A breakdown of the total elapsed time for the numerical analysis is considered in a detail. It is found that the GPGPU technology can accelerate the solution more than one hundred times faster by employing a simple rectangular mesh and coding to reduce a memory access overhead.

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Author Biographies

Takashi Nakamura

Department of Global Engineering for Development, Environment and Society, Tokyo Institute of
Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Shun Murakami

IDAJ Co., LTD., 2-2-1-1 Minato Mirai, Nishi-ku, Yokohama 220-8137, Japan

Lun Sambo

Department of Rural Engineering, Institute of Technology of Cambodia, PO Box 86, Russian
Federation Blvd., Phnom Penh, Cambodia

Hideto Fujii

Department of Food, Life and Environmental Sciences, Yamagata University, 1-23 Wakaba-machi,
Tsuruoka, Yamagata 997-8555, Japan

Published

Vol 23 No 1, Jan 31, 2019

How to Cite

[1]
T. Nakamura, S. Murakami, L. Sambo, and H. Fujii, “Efficacy of a GPGPU-Acceleration to Inundation Flow Simulation in Tonle Sap Lake in Cambodia”, Eng. J., vol. 23, no. 1, pp. 151-169, Jan. 2019.