Leveraging OpenMP Tasks for Efficient Parallel Modeling of the Elastic Eave Propagation in Multi-mesh Problems

Authors

  • Nikolay I. Khokhlov Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation; Scientific Research Institute for System Analysis of the National Research Centre “Kurchatov Institute”, Moscow, Russian Federation https://orcid.org/0000-0002-2460-0137
  • Vladislav O. Stetsyuk Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation

DOI:

https://doi.org/10.14529/jsfi240407

Keywords:

grid-characteristic method, OpenMP, task based parallelism, overset meshes, geological fractures

Abstract

This paper presents a new algorithm for parallelizing the grid-characteristic method in sharedmemory systems. The OpenMP task parallelism mechanism is used for parallelization. A modification of the grid-characteristic method is considered that uses a set of overlapped grids to determine a complex heterogeneous structure of the computational domain. The complexity of parallelizing the algorithm is represented by the presence of many different-sized grids. The proposed algorithm is described and compared with basic parallelization algorithms. Basic algorithms mean separate parallelization within each computational grid using the loop parallelization mechanism. An analysis of the efficiency of the post-doubling and parallel algorithms is performed. The advantage of the proposed algorithm for a number of problems is demonstrated. The results of testing and calculating the propagation of wave disturbances in a fractured layer are presented. Each crack in the example is specified by a separate computational grid, which significantly increases the multi-scale problem and the number of computational grids. Work is underway to transfer the algorithm to the three-dimensional case.

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Published

2025-02-04

How to Cite

Khokhlov, N. I., & Stetsyuk, V. O. (2025). Leveraging OpenMP Tasks for Efficient Parallel Modeling of the Elastic Eave Propagation in Multi-mesh Problems. Supercomputing Frontiers and Innovations, 11(4), 78–91. https://doi.org/10.14529/jsfi240407