WAVEWATCH III Hybrid Parallelization for Azov Sea Wave Modeling

Authors

  • Alexander I. Sukhinov Don State Technical University, Rostov-on-Don, Russian Federation https://orcid.org/0000-0002-5875-1523
  • Elena A. Protsenko Chekhov Taganrog Institute (Branch of Rostov State University of Economics), Taganrog, Russian Federation https://orcid.org/0000-0001-7911-3558
  • Sofya V. Protsenko Chekhov Taganrog Institute (Branch of Rostov State University of Economics), Taganrog, Russian Federation

DOI:

https://doi.org/10.14529/jsfi240104

Keywords:

multiple-cell grid, WAVEWATCH III, hybrid parallelization, parallel numerical implementation

Abstract

The article examines potential applications of WAVEWATCH III (WW3), the thirdgeneration wind-wave model. This study delves into the implementation of hybrid parallelization (MPI-OpenMP) and the development of multiple-cell grids tailored for the Azov Sea region. It elucidates fundamental equations of the model, their discretization, and software execution. The multiple-cell grid strategy employs high-resolution cells within the region of interest, gradually increasing cell density in other areas to optimize memory consumption. A 6-level multiple-cell grid was specifically crafted for the Azov Sea, with an algorithm outlined for its generation incorporating two refinement methods. This algorithm enables the creation of refined multiple-cell grids near shorelines at varying levels, along with the capability to refine grid structures in arbitrary zones. Additionally, the article presents hybrid parallelization techniques for the wave spectral component (MPI-OpenMP), assessing scalability in both MPI and hybrid deployments. The WW3 model offers a multigrid option facilitating parallel operation of subdomains akin to domain decomposition, while ensuring parallelization of each subnet via the component decomposition method.

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Published

2024-06-06

How to Cite

Sukhinov, A. I., Protsenko, E. A., & Protsenko, S. V. (2024). WAVEWATCH III Hybrid Parallelization for Azov Sea Wave Modeling. Supercomputing Frontiers and Innovations, 11(1), 81–96. https://doi.org/10.14529/jsfi240104