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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2023, Volume 63, Number 12, Pages 1993–2015
DOI: https://doi.org/10.31857/S0044466923120086 (Mi zvmmf11666) 		 
This article is cited in 3 scientific papers (total in 3 papers)

Mathematical physics

Data parallelization algorithms for the direct simulation Monte Carlo method for rarefied gas flows on the basis of OpenMP technology

N. Yu. Bykovab, S. A. Fyodorovab

a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, 119333, Moscow, Russia
b Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia
Citations (3)
DOI: https://doi.org/10.31857/S0044466923120086
Abstract: A data parallelization algorithm for the direct simulation Monte Carlo method for rarefied gas flows is considered. The scaling of performance of the main algorithm procedures are analyzed. Satisfactory performance scaling of the parallel particle indexing procedure is shown, and an algorithm for speeding up the operation of this procedure is proposed. Using examples of solving problems of free flow and flow around a cone for a 28-core node with shared memory, an acceptable speedup of the entire algorithm was obtained. The efficiency of the data parallelization algorithm and the computational domain decomposition algorithm for free flow is compared. Using the developed parallel code, a study of the supersonic rarefied flow around a cone is carried out.
Key words: direct simulation Monte Carlo method, parallel algorithms, data parallelization, OpenMP, rarefied gas, flow around cone.
Funding agency Grant number
Russian Science Foundation 22-11-00078
This work was supported by the Russian Science Foundation, project no. 22-11-00078.
Received: 26.06.2023
Revised: 05.07.2023
Accepted: 22.08.2023
English version:
Computational Mathematics and Mathematical Physics, 2023, Volume 63, Issue 12, Pages 2275–2296
DOI: https://doi.org/10.1134/S0965542523120072
Bibliographic databases:
Document Type: Article
UDC: 519.635
Language: Russian
Citation: N. Yu. Bykov, S. A. Fyodorov, “Data parallelization algorithms for the direct simulation Monte Carlo method for rarefied gas flows on the basis of OpenMP technology”, Zh. Vychisl. Mat. Mat. Fiz., 63:12 (2023), 1993–2015; Comput. Math. Math. Phys., 63:12 (2023), 2275–2296
Citation in format AMSBIB
\Bibitem{BykFyo23}
\by N.~Yu.~Bykov, S.~A.~Fyodorov
\paper Data parallelization algorithms for the direct simulation Monte Carlo method for rarefied gas flows on the basis of OpenMP technology
\jour Zh. Vychisl. Mat. Mat. Fiz.
\yr 2023
\vol 63
\issue 12
\pages 1993--2015
\mathnet{http://mi.mathnet.ru/zvmmf11666}
\crossref{https://doi.org/10.31857/S0044466923120086}
\elib{https://elibrary.ru/item.asp?id=54912956}
\transl
\jour Comput. Math. Math. Phys.
\yr 2023
\vol 63
\issue 12
\pages 2275--2296
\crossref{https://doi.org/10.1134/S0965542523120072}
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  • https://www.mathnet.ru/eng/zvmmf/v63/i12/p1993
    • This publication is cited in the following 3 articles:
    1. Sanghun Kim, Eunji Jun, “A particle Fokker–Planck method for rarefied gas flows of monatomic mixtures”, Physics of Fluids, 37:1 (2025)  crossref
    2. Ziheng Zhou, Bijiao He, Guobiao Cai, Huiyan Weng, Weizong Wang, Lihui Liu, Shengfei Shang, Baiyi Zhang, “Real-time vacuum plume flow field reconstruction during lunar landings based on deep learning”, Physics of Fluids, 36:7 (2024)  crossref
    3. Baiyi Zhang, Guobiao Cai, Da Gao, Huiyan Weng, Weizong Wang, Bijiao He, “Development of convolutional neural network-based surrogate model for three-dimensional vacuum plume prediction via direct simulation Monte Carlo method”, Physics of Fluids, 36:7 (2024)  crossref
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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