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GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM

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Parallel Processing and Applied Mathematics (PPAM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13826))

Abstract

In this paper we describe the computational framework for GPU-based molecular dynamics of turbulent flows. The framework is based on the open-source molecular dynamics library OpenMM. The implementation of a special type of open boundary conditions is presented together with a generic case of a turbulent flow of Lennard-Jones liquid. We compare the computational efficiency of OpenMM with another popular MD library LAMMPS and other legacy MD programs used for studying turbulence.

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Acknowledgment

This research was supported in part through computational resources of the Supercomputer Centre of JIHT RAS and HPC facilities at HSE University. The study was supported by the Russian Science Foundation (project no. 20-71-10127).

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Authors and Affiliations

  1. Joint Institute for High Temperatures of RAS, Moscow, Russian Federation

    Daniil Pavlov, Daniil Kolotinskii & Vladimir Stegailov

  2. Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russian Federation

    Daniil Pavlov, Daniil Kolotinskii & Vladimir Stegailov

  3. National Research University Higher School of Economics, Moscow, Russian Federation

    Vladimir Stegailov

Authors
  1. Daniil Pavlov
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  2. Daniil Kolotinskii
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  3. Vladimir Stegailov
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Corresponding author

Correspondence to Daniil Pavlov .

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Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, TN, USA

    Jack Dongarra

  3. University of Southern California, Marina del Rey, CA, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Pavlov, D., Kolotinskii, D., Stegailov, V. (2023). GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2022. Lecture Notes in Computer Science, vol 13826. Springer, Cham. https://doi.org/10.1007/978-3-031-30442-2_26

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  • DOI: https://doi.org/10.1007/978-3-031-30442-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30441-5

  • Online ISBN: 978-3-031-30442-2

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