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[Licence| Download | New Version Template] aetn_v1_0.tar.gz(25868 Kbytes)
Manuscript Title: Accelerating Dissipative Particle Dynamics Simulations on GPUs: Algorithms, Numerics and Applications
Authors: Yu-Hang Tang, George Em Karniadakis
Program title: GPU-accelerated DPD Package for LAMMPS
Catalogue identifier: AETN_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 185(2014)2809
Programming language: C/C++, CUDA C/C++, MPI.
Computer: Any computers having nVidia GPGPUs with compute capability 3.0.
Operating system: Linux.
Has the code been vectorised or parallelized?: Yes. Number of processors used: 1024 16-core CPUs and 1024 GPUs
RAM: 500 Mbytes host memory, 2 Gbytes device memory
Supplementary material: The data for the examples discussed in the manuscript is available for download.
Keywords: DPD CUDA LAMMPS spontaneous vesicle formation.
PACS: 47.11.Mn, 87.10.Tf, 87.15.ap, 68.65.-k.
Classification: 6.5, 12, 16.1, 16.11.

Nature of problem:
Particle-based simulation of mesoscale systems involving nano/micro-fluids, polymers and spontaneous self-assembly process.

Solution method:
The system is approximated by a number of coarse-grained particles interacting through pairwise potentials and bonded potentials. Classical mechanics is assumed following Newton's laws. The evolution of the system is integrated using a time-stepping scheme such as Velocity-Verlet.

The code runs only on CUDA GPGPUs with compute capability 3.0.

Unusual features:
Fully implemented on GPGPUs with signicant speedup.

Running time:
78 hours using 1024 GPGPUs for simulating a 128-million-particle system for 18.4 million time steps.