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T-HySEA (Tsunami-HySEA)

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Code name T-HySEA (Tsunami-HySEA)
Developer(s)

Manuel J. Castro Díaz

Jorge Macías Sánchez

Marc de la Asunción

Contact person: Jorge Macías Sánchez

Link

https://edanya.uma.es/hysea/

Short description

T-HySEA solves the 2D shallow water equations on hydrostatic and dispersive versions. T-HySEA is based on a high-order Finite Volume (FV) discretization (hydrostatic) with Finite Differences (FD) for the dispersive version on two-way structured nested meshes in spherical coordinates. Initial conditions from the Okada model or initial deformation, synchronous and asynchronous multi-Okada, rectangular and triangular faults.

Original code level

3

Current code level

8-9

Pilot(s) involved

PD2, PD7,PD8

Main results and References

MAIN RESULTS:

  • Code Audit driven improvements.
  • Better load balancing.

  • Resuming a stored simulation.

  • Asynchronous NetCDF file writing.

  • Asynchronous CPU-GPU memory transfers.

  • Direct GPU to GPU data transfers using GPUDirect in architectures that support it.

  • Added compression of the NetCDF output files.

  • Support for bigger meshes by using int64_t datatype.

  • Developed a Monte-Carlo version capable of performing many simulations in a single execution, one on each GPU.

REFERENCES:

Macías, J., Castro, M.J., Ortega, S., Escalante, C., González-Vida, J.M. (2017). Performance benchmarking of Tsunami-HySEA model for NTHMP's inundation mapping activities. Pure and Applied Geophysics , 1-37.  [doi: 10.1007/s00024-017-1583-1]

Macías, J., Castro, M.J., Escalante, C. (2020).  Performance assessment of Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Laboratory data. Coastal Engineering, 158, 103667, ISSN 0378-3839, [doi: 10.1016/j.coastaleng.2020.103667].


Macías, J., Castro, M.J. Ortega, S., and González-Vida, J.M., (2020).  Performance assessment of Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Field cases. Ocean Modeling, 152, 101645, ISSN 1463-5003, [doi: 10.1016/j.ocemod.2020.101645].

Performance results
  • Strong scaling reaches up to 87 % efficiency using 32 GPUs with direct GPU to GPU data transfers.

  • Use of direct GPU to GPU data transfers improves runtimes and scaling up to a 20 %.

  • Asynchronous file writing reduces the runtimes up to a 50 % for high frequency saving.

The ChEESE project has received funding from the European Union’s Horizon 2020
research and innovation programme under the grant agreement Nº 823844. All rights reserved. Legal Notice.
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