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Title: Accelerating dissipative particle dynamics simulations for soft matter systems

Abstract

Dissipative particle dynamics (DPD) is a coarse-grained particle-based simulation method that offers microscopic-scale insights into soft matter systems. We present an efficient implementation of a DPD model for graphical processing units (GPUs). As implemented in the LAMMPS molecular dynamics package, it can run effectively on current-generation supercomputers which often have hybrid nodes containing multi-core CPUs and (one or more) GPUs. Using efficient communication of information between the CPUs and GPUs, DPD interactions can be computed on the GPU while other portions of a full simulation model (boundary conditions, constraints, bonded interactions, diagnostic calculations, etc.) can be performed on the CPU. Our GPU-enhanced runs show a speedup of up to 9.5× versus many-core CPU simulations, and can run scalably across thousands of compute nodes. We briefly discuss how the new GPU implementation was validated against the CPU version for thermodynamics, diffusion, and hydrodynamic behavior. We also highlight large-scale models which the faster DPD implementation has enabled, for studies of monolayer self-assembly and thin-film instabilities.

Authors:
 [1];  [2]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1565290
Alternate Identifier(s):
OSTI ID: 1250303; OSTI ID: 1820891
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 100; Journal Issue: PB; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; Materials Science; Dissipative particle dynamics; LAMMPS; GPU acceleration; Hybrid CPU/GPU; Hybrid MPI/GPU; High-performance computing

Citation Formats

Nguyen, Trung Dac, and Plimpton, Steven J. Accelerating dissipative particle dynamics simulations for soft matter systems. United States: N. p., 2014. Web. doi:10.1016/j.commatsci.2014.10.068.
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Nguyen, Trung Dac, & Plimpton, Steven J. Accelerating dissipative particle dynamics simulations for soft matter systems. United States. https://doi.org/10.1016/j.commatsci.2014.10.068
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Nguyen, Trung Dac, and Plimpton, Steven J. Wed . "Accelerating dissipative particle dynamics simulations for soft matter systems". United States. https://doi.org/10.1016/j.commatsci.2014.10.068. https://www.osti.gov/servlets/purl/1565290.
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@article{osti_1565290,
title = {Accelerating dissipative particle dynamics simulations for soft matter systems},
author = {Nguyen, Trung Dac and Plimpton, Steven J.},
abstractNote = {Dissipative particle dynamics (DPD) is a coarse-grained particle-based simulation method that offers microscopic-scale insights into soft matter systems. We present an efficient implementation of a DPD model for graphical processing units (GPUs). As implemented in the LAMMPS molecular dynamics package, it can run effectively on current-generation supercomputers which often have hybrid nodes containing multi-core CPUs and (one or more) GPUs. Using efficient communication of information between the CPUs and GPUs, DPD interactions can be computed on the GPU while other portions of a full simulation model (boundary conditions, constraints, bonded interactions, diagnostic calculations, etc.) can be performed on the CPU. Our GPU-enhanced runs show a speedup of up to 9.5× versus many-core CPU simulations, and can run scalably across thousands of compute nodes. We briefly discuss how the new GPU implementation was validated against the CPU version for thermodynamics, diffusion, and hydrodynamic behavior. We also highlight large-scale models which the faster DPD implementation has enabled, for studies of monolayer self-assembly and thin-film instabilities.},
doi = {10.1016/j.commatsci.2014.10.068},
journal = {Computational Materials Science},
number = PB,
volume = 100,
place = {United States},
year = {Wed Nov 26 00:00:00 EST 2014},
month = {Wed Nov 26 00:00:00 EST 2014}
}
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https://doi.org/10.1016/j.commatsci.2014.10.068
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    Effect of solvent quality on Poiseuille flow of polymer solutions in microchannels: A dissipative particle dynamics study
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    The in-silico lab-on-a-chip: petascale and high-throughput simulations of microfluidics at cell resolution
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    • Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '15
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