1,715 research outputs found
Improved neighbor list algorithm in molecular simulations using cell decomposition and data sorting method
An improved neighbor list algorithm is proposed to reduce unnecessary
interatomic distance calculations in molecular simulations. It combines the
advantages of Verlet table and cell linked list algorithms by using cell
decomposition approach to accelerate the neighbor list construction speed, and
data sorting method to lower the CPU data cache miss rate, as well as partial
updating method to minimize the unnecessary reconstruction of the neighbor
list. Both serial and parallel performance of molecular dynamics simulation are
evaluated using the proposed algorithm and compared with those using
conventional Verlet table and cell linked list algorithms. Results show that
the new algorithm outperforms the conventional algorithms by a factor of 2~3 in
cases of both small and large number of atoms.Comment: 14 pages, 7 figures. Submitted to Computer Physics Communication
Efficiency of linked cell algorithms
The linked cell list algorithm is an essential part of molecular simulation
software, both molecular dynamics and Monte Carlo. Though it scales linearly
with the number of particles, there has been a constant interest in increasing
its efficiency, because a large part of CPU time is spent to identify the
interacting particles. Several recent publications proposed improvements to the
algorithm and investigated their efficiency by applying them to particular
setups. In this publication we develop a general method to evaluate the
efficiency of these algorithms, which is mostly independent of the parameters
of the simulation, and test it for a number of linked cell list algorithms. We
also propose a combination of linked cell reordering and interaction sorting
that shows a good efficiency for a broad range of simulation setups.Comment: Submitted to Computer Physics Communications on 22 December 2009,
still awaiting a referee repor
Efficient Implementations of Molecular Dynamics Simulations for Lennard-Jones Systems
Efficient implementations of the classical molecular dynamics (MD) method for
Lennard-Jones particle systems are considered. Not only general algorithms but
also techniques that are efficient for some specific CPU architectures are also
explained. A simple spatial-decomposition-based strategy is adopted for
parallelization. By utilizing the developed code, benchmark simulations are
performed on a HITACHI SR16000/J2 system consisting of IBM POWER6 processors
which are 4.7 GHz at the National Institute for Fusion Science (NIFS) and an
SGI Altix ICE 8400EX system consisting of Intel Xeon processors which are 2.93
GHz at the Institute for Solid State Physics (ISSP), the University of Tokyo.
The parallelization efficiency of the largest run, consisting of 4.1 billion
particles with 8192 MPI processes, is about 73% relative to that of the
smallest run with 128 MPI processes at NIFS, and it is about 66% relative to
that of the smallest run with 4 MPI processes at ISSP. The factors causing the
parallel overhead are investigated. It is found that fluctuations of the
execution time of each process degrade the parallel efficiency. These
fluctuations may be due to the interference of the operating system, which is
known as OS Jitter.Comment: 33 pages, 19 figures, add references and figures are revise
An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs
Maximizing the performance potential of the modern day GPU architecture
requires judicious utilization of available parallel resources. Although
dramatic reductions can often be obtained through straightforward mappings,
further performance improvements often require algorithmic redesigns to more
closely exploit the target architecture. In this paper, we focus on efficient
molecular simulations for the GPU and propose a novel cell list algorithm that
better utilizes its parallel resources. Our goal is an efficient GPU
implementation of large-scale Monte Carlo simulations for the grand canonical
ensemble. This is a particularly challenging application because there is
inherently less computation and parallelism than in similar applications with
molecular dynamics. Consistent with the results of prior researchers, our
simulation results show traditional cell list implementations for Monte Carlo
simulations of molecular systems offer effectively no performance improvement
for small systems [5, 14], even when porting to the GPU. However for larger
systems, the cell list implementation offers significant gains in performance.
Furthermore, our novel cell list approach results in better performance for all
problem sizes when compared with other GPU implementations with or without cell
lists.Comment: 30 page
Improved O(N) neighbor list method using domain decomposition and data sorting
The conventional Verlet table neighbor list algorithm is improved to reduce the number of unnecessary inter-atomic distance calculations in molecular simulations involving large amount of atoms. Both of the serial and parallelized performance of molecular dynamics simulation are evaluated using the new algorithm and compared with those using the conventional Verlet table and cell-linked list algorithm. Results show that the new algorithm significantly improved the performance of molecular dynamics simulation compared with conventional neighbor list maintaining and utilizing algorithms in serial programs as well as parallelized programs.Singapore-MIT Alliance (SMA
Interstitial-Scale Modeling of Packed-Bed Reactors
Packed-beds are common to adsorption scrubbers, packed bed reactors, and trickle-bed reactors widely used across the petroleum, petrochemical, and chemical industries. The micro structure of these packed beds is generally very complex and has tremendous influence on heat, mass, and momentum transport phenomena on the micro and macro length scales within the bed. On a reactor scale, bed geometry strongly influences overall pressure drop, residence time distribution, and conversion of species through domain-fluid interactions. On the interstitial scale, particle boundary layer formation, fluid to particle mass transfer, and local mixing are controlled by turbulence and dissipation existing around packed particles. In the present research, a CFD model is developed using OpenFOAM: www.openfoam.org) to directly resolve momentum and scalar transport in both laminar and turbulent flow-fields, where the interstitial velocity field is resolved using the Navier-Stokes equations: i.e. no pseudo-continuum based assumptions. A discussion detailing the process of generating the complex domain using a Monte-Carlo packing algorithm is provided, along with relevant details required to generate an arbitrary polyhedral mesh describing the packed-bed. Lastly, an algorithm coupling OpenFOAM with a linear system solver using the graphics processing unit: GPU) computing paradigm was developed and will be discussed in detail
SPH with the multiple boundary tangent method
In this article, we present an improved solid boundary treatment formulation for the smoothed particle hydrodynamics (SPH) method. Benchmark simulations using previously reported boundary treatments can suffer from particle penetration and may produce results that numerically blow up near solid boundaries. As well, current SPH boundary approaches do not properly treat curved boundaries in complicated flow domains. These drawbacks have been remedied in a new boundary treatment method presented in this article, called the multiple boundary tangent (MBT) approach. In this article we present two important benchmark problems to validate the developed algorithm and show that the multiple boundary tangent
treatment produces results that agree with known numerical and experimental solutions. The two benchmark problems chosen are the lid-driven cavity problem, and flow over a cylinder. The SPH solutions using the MBT approach and the results from literature are in very good agreement. These solutions involved
solid boundaries, but the approach presented herein should be extendable to time-evolving, free-surface boundaries
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