Application of the Alternating Triangular Iterative Method to Solving Shallow Water Hydrodynamics Problems with Graphics Accelerators
PDF (Russian)

Keywords

simulation
hydrodynamic processes
shallow water

How to Cite

1.
Litvinov V.N., Gracheva N.N., Shabaev E.A. Application of the Alternating Triangular Iterative Method to Solving Shallow Water Hydrodynamics Problems with Graphics Accelerators // Russian Journal of Cybernetics. 2022. Vol. 3, № 1. P. 53-57. DOI: 10.51790/2712-9942-2022-3-1-8.

Abstract

Forecasting environmental disasters, both artificial and natural, is currently based on advances in simulation. Fast forecasting is very difficult without the use of parallel computing and supercomputer technologies. The large volume of information to be processed and the complexity of calculations require to use of computing clusters with graphics cards to increase the computing performance and the data processing rate. This paper deals with the development of a software module in CUDA C to simulate the hydrodynamic processes in shallow water bodies, including the solution of systems of high-dimensional grid equations during discretization. To improve computing performance, graphics accelerators take a part of the load. To enable this, we developed an algorithm for parallel calculations and implemented it as a software module. As a result of computational experiments, the optimal 2D configuration of streams in a computational unit run on a single streaming multiprocessor was determined. The proposed algorithm and software module enables more efficient use of GPU computational resources when solving computationally intensive hydro physics problems.

https://doi.org/10.51790/2712-9942-2022-3-1-8
PDF (Russian)

References

Zheng L., Gerya T., Knepley M., Yuen D., Zhang H., Shi Ya. GPU Implementation of Multigrid Solver for Stokes Equation with Strongly Variable Viscosity. In: Yuen D., Wang L., Chi X., Johnsson L., Ge W., Shi Y. (eds) GPU Solutions to Multi-scale Problems in Science and Engineering. Lecture Notes in Earth System Sciences. Springer, Berlin, Heidelberg. 2013. https://doi.org/10.1007/978-3-642-16405-7_21.

Xue W., Roy C. J. Multi-GPU Performance Optimization of a Computational Fluid Dynamics Code Using OpenACC. Concurrency and Computation Practice and Experience. 2021;33(5).

Oyarzun G., Borrell R., Gorobets A., Oliva A. MPI-CUDA Sparse Matrix–Vector Multiplication for the Conjugate Gradient Method with an Approximate Inverse Preconditioner. Computers and Fluids. 2014;92:244-252.

Nagatake T., Kunugi T. Application of GPU to Computational Multiphase Fluid Dynamics. IOP Conference Series: Materials Science and Engineering. 2010;10.

Belova Y., Chistyakov A., Nikitina A., Litvinov V. Mathematical Modeling of Sustainable Coastal Systems Development Scenarios Based on Game-Theoretic Concepts of Hierarchical Management Using Supercomputer Technologies. In: Voevodin V., Sobolev S. (eds) Supercomputing. RuSCDays 2020. Communications in Computer and Information Science. 2020;1331. Springer, Cham. Режим доступа: https://doi.org/10.1007/978-3-030-64616-5_22.

Сухинов А. И., Атаян А. М., Белова Ю. В., Литвинов В. Н., Никитина А. В., Чистяков А. Е. Обработка данных натурных измерений экспедиционных исследований для математического моделирования гидродинамических процессов Азовского моря. Вычислительная механика сплошных сред. 2020;13(2):161-174. Режим доступа: https://doi.org/https://doi.org/10.7242/1999-6691/2020.13.2.13.

Сухинов А. И., Чистяков А. Е., Литвинов В. Н., Никитина А. В., Белова Ю. В., Филина А. А. Вычислительные аспекты математического моделирования гидробиологических процессов в мелководном водоеме. Вычислительные методы и программирование. 2020;21(4):452-469. DOI: 10.26089/NumMet.v21r436.

Xue W., C. W. Jackson, Roy C. J. Multi-CPU/GPU Parallelization, Optimization and Machine Learning based Autotuning of Structured Grid CFD Codes. 2018 AIAA Aerospace Sciences Meeting. P. 0362. DOI: 10.2514/6.2018-0362.

Downloads

Download data is not yet available.