TY - JOUR
T1 - Accelerating dissipative particle dynamics simulations for soft matter systems
AU - Nguyen, Trung Dac
AU - Plimpton, Steven J.
N1 - Funding Information:
The GPU and CPU DPD models described here are part of the open-source LAMMPS distribution, available for download at http://lammps.sandia.gov . T.D.N. thanks W. M. Brown for helpful discussion regarding the implementation of the GPU package and Arnold N. Tharrington for comments on the random number generators on the GPU. This research used resources of the Leadership Computing Facility at Oak Ridge National Laboratory and was conducted under the auspices of the Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC .
Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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 9.5x 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.
AB - 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 9.5x 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.
KW - Dissipative particle dynamics
KW - GPU acceleration
KW - High-performance computing
KW - Hybrid CPU/GPU
KW - Hybrid MPI/GPU
KW - LAMMPS
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U2 - 10.1016/j.commatsci.2014.10.068
DO - 10.1016/j.commatsci.2014.10.068
M3 - Article
AN - SCOPUS:84923579350
VL - 100
SP - 173
EP - 180
JO - Computational Materials Science
JF - Computational Materials Science
SN - 0927-0256
IS - PB
ER -