JDR Vol.13 No.2 pp. 234-244
doi: 10.20965/jdr.2018.p0234


A Real-Time Tsunami Inundation Forecast System Using Vector Supercomputer SX-ACE

Akihiro Musa*1,*2,†, Takashi Abe*3, Takuya Inoue*3,*4, Hiroaki Hokari*2, Yoichi Murashima *3,*4, Yoshiyuki Kido*5, Susumu Date*5, Shinji Shimojo*5, Shunichi Koshimura*3, and Hiroaki Kobayashi*6

*1Cyberscience Center, Tohoku University
6-3 Aramaki Aza Aoba, Aoba-ku, Sendai, Japan

Corresponding author

*2NEC Corporation, Tokyo, Japan

*3International Research Institute of Disaster Science, Tohoku University, Sendai, Japan

*4Kokusai Kogyo Co.,LTD., Tokyo, Japan

*5Cybermedia Center, Osaka University, Osaka, Japan

*6Graduate School of Information Sciences, Tohoku University, Sendai, Japan

October 31, 2017
February 19, 2018
Online released:
March 19, 2018
March 20, 2018
tsunami, real-time simulation, supercomputer, system performance

Tsunami disasters can cause serious casualties and damage to social infrastructures. An early understanding of disaster states is required in order to advise evacuations and plan rescues and recoveries. We have developed a real-time tsunami inundation forecast system using a vector supercomputer SX-ACE. The system can complete a tsunami inundation and damage estimation for coastal city regions at the resolution of a 10 m grid size in under 20 minutes, and distribute tsunami inundation and infrastructure damage information to local governments in Japan. We also develop a new configuration for the computational domain, which is changed from rectangles to polygons and called a polygonal domain, in order to effectively simulate in the entire coast of Japan. Meanwhile, new supercomputers have been developed, and their peak performances have increased year by year. In 2016, a new Xeon Phi processor called Knights Landing was released for high-performance computing. In this paper, we present an overview of our real-time tsunami inundation forecast system and the polygonal domain, which can decrease the amount of computation in a simulation, and then discuss its performance on a vector supercomputer SX-ACE and a supercomputer system based on Intel Xeon Phi. We also clarify that the real-time tsunami inundation forecast system requires the efficient vector processing of a supercomputer with high-performance cores.

Cite this article as:
A. Musa, T. Abe, T. Inoue, H. Hokari, Y. Murashima, Y. Kido, S. Date, S. Shimojo, S. Koshimura, and H. Kobayashi, “A Real-Time Tsunami Inundation Forecast System Using Vector Supercomputer SX-ACE,” J. Disaster Res., Vol.13 No.2, pp. 234-244, 2018.
Data files:
  1. [1] The 2011 Tohoku Earthquake Tsunami Joint Survey (TTJS) Group, “Nationwide field survey of the 2011 off the Pacific coast of Tohoku earthquake tsunami,” Journal of Japan Society of Civil Engineers, Ser. B2, Vol.67, No.1, pp. 63–66, November 2011.
  2. [2] F. Imamura and S. Anawat, “Damage due to the 2011 Tohoku Earthquake Tsunami and its Lessons for Future Mitigation,” In Proceedings of the International Symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, Tokyo, Japan, March 2012.
  3. [3] N. Mori, T. Takahashi, and H. Yanagisawa, “Survey of 2011 Tohoku earthquake tsunami inundation,” Geophysical Research Letters, Vol.38 L00G14, 2016, doi:10.1029/2011GL049210.
  4. [4] K. Goto, K. Fujima, D. Sugawara, S. Fujino, K. Imai, R. Tsudaka, T. Abe, and T. Haraguchi, “Filed measurements and numerical modeling for the run-up heights and inundation distances of the 2011 Tohoku-oki tsunami at Sendai Plain, Japan,” Earth Planets Space, Vol.64, pp. 1247–1257, 2012.
  5. [5] T. Arikawa and T. Tomita, “Development of High Precision Tsunami Runup Calculation Method Based on a Hierarchical Simulation,” Journal of Disaster Research, Vol.11, No.4, pp. 639–646, 2016.
  6. [6] S. Koshimura, “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation,” Journal of Disaster Research, Vol.11, No.2, pp. 164–174, 2016.
  7. [7] S. Koshimura, R. Hino, Y. Ohta, H. Kobayashi, A. Musa, and Y. Murashima, “Real-time tsunami inundation forecasting and damage estimation method by fusion of real-time crustal deformation monitoring and high-performance computing,” In The presentation at 26th International Union of Geodesy and Geophysics General Assembly 2015, June 2015.
  8. [8] H. Kobayashi, “A Case Study of Urgent Computing on SX-ACE: Design and Development of a Real-Time Tsunami Inundation Analysis System for Disaster Prevention and Mitigation,” In M. Resch et al. (Eds.), Sustained Simulation Performance 2016, pp. 131–138. Springer-Verlag, 2016.
  9. [9] A. Sodani, R. Gramunt, J. Corbal, H.-S. Kim, K. Vinod, S. Chinthamani, S. Hutsell, R. Agarwal, and Y.-C. Liu, “Knights Landing: Second-Generation Intel Xeon Phi Product,” IEEE Micro, Vol.36, pp. 34–46, 2016.
  10. [10] M. T. Satria, T.-J. H. B. Huang, Y.-L. Chang, and W.-Y. Liang, “GPU acceleration of tsunami propagation Model,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.5, No.3, pp. 1014–1023, 2012.
  11. [11] R. Amouzgar, Q. Liang, P. J. Clarke, T. Yasuda, and H. Mase, “Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU),” International Journal of Offshore and Polar Engineering, Vol.26, No.2, pp. 154–160, 2016.
  12. [12] S. Christgau, J. Spazier, and B. Schnor, “A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Difference Multi-Core Architectures and Programming Models,” Technical report, Institute for Computer Science, University of Potsdam, 2015, [accessed December, 2017]
  13. [13] IUGG/IOC Time Project, “IUGG/IOC TIME PROJECT: Numerical method of Tsunami simulation with the leap-frog scheme,” UNESCO, 1997.
  14. [14] K. Nagasu, K. Sane, F. Kono, and N. Nakasato, “FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis,” Journal of Parallel and Distributed Computing, Vol.106, pp. 153–169, August 2016,
  15. [15] Y. Oishi, F. Imamura, and D. Sugawara, “Near-field tsunami inundation forecast using the parallel TUNAMI-N2 model: Application to the 2011 Tohoku-Oki earthquake combined with source inversions,” Geophysical Research Letters, Vol.42, No.4, pp. 1083–1091, 2015,
  16. [16] F. Imamura, A. C. Yalciner, and G. Ozyurt, “TSUNAMI MODELING MANUAL (TUNAMI model),” Tohoku University, Sendai, Japan, April 2006, [accessed October, 2017]
  17. [17] M. Yokokawa, “The K Computer and its Application,” In 2012 Third International Conference on Networking and Computing (ICNC), pp. 21–22, Dec 2012, doi:10.1109/ICNC.2012.13.
  18. [18] A. Musa, H. Kuba, and O. Kamoshida, “Earthquake and Tsunami Warning System for Natural Disaster Prevention,” In M. Resch et al. (Eds.), Sustained Simulation Performance 2012, pp. 81–91. Springer-Verlag, 2012.
  19. [19] Y. Ohta, T. Kobayashi, H. Tsushima, S. Miura, R. Hino, T. Takasu, H. Fujimoto, T. Iinuma, K. Tachibana, T. Demachi, T. Sato, M. Ohzono, and N. Umino, “Quasi real-time fault model estimation for near-field tsunami forecasting based on RTK-GPS analysis: Application to the 2011 Tohoku-Oki earthquake (Mw 9.0),” Journal of Geophysical Research, Vol.117, B02311, doi:10.1029/2011JB008750, 2012.
  20. [20] S. Koshimura, T. Oie, H. Yanagisawa, and F. Imamura, “Developing fragility functions for tsunami damage estimation using numerical model and post-tsunami data from Banda Aceh, Indonesia,” Coastal Engineering Journal, JSCE, Vol.51, No.3, pp. 243–273, 2009.
  21. [21] C. Goto, Y. Ogawa, N. Shuto, and F. Imamura, “Numerical method of Tsunami simulation with the leap-frog scheme,” Technical report, UNESCO, 1997, [accessed October, 2017]
  22. [22] Y. Okada, “Internal Deformation due to Shear and Tensile Faults in a Half-Space,” Bulletin of the Seismological Society of America, Vol.82, No.2, pp. 1018–1040, 1992.
  23. [23] T. Furumura, K. Imai, and T. Maeda, “A revised tsunami source model for the 1707 Hoei earthquake and simulation of tsunami inundation of Ryujin Lake, Kyushu, Japan,” Journal of Geophysical Research, Vol.116, No.B02308, 2011, doi:10.1029/2010JB007918.
  24. [24] S. Momose, “SX-ACE, Brand-New Vector Supercomputer for Higher Sustained Performance I,” In M. Resch et al. (Eds.), Sustained Simulation Performance 2014, pp. 57–67, Springer-Verlag, 2014.
  25. [25] D. Kroft, “Lockup-Free Instruction Fetch/Prefetch Cache Organization,” In The 8th International Symposium on Computer Architecture (ISCA), PA, USA, 1981.
  26. [26] A. Musa, Y. Sato, R. Egawa, H. Takizawa, K. Okabe, and H. Kobayashi, “Characteristics of an On-Chip Cache on NEC Vector Architecture,” Interdisciplinary Information Sciences, Graduate School of Information Sciences, Tohoku University, Vol.15, No.1, pp. 51–66, 2009.
  27. [27] T. Soga, A. Musa, Y. Shimomura, R. Egawa, K. Itakura, H. Takizawa, K. Okabe, and H. Kobayashi, “Performance evaluation NEC SX-9 using real science and engineering applications,” In Proceedings of the ACM/IEEE International Conference on High Performance Computing, Networking, Storage and Analysis (SC09), Portland, Oregon, November 2009.

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