Parallel atomistic simulations

Heffelfinger, Grant S.

doi:10.1016/s0010-4655(00)00050-3

Cited by 71 publications

(44 citation statements)

References 128 publications

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“…Because the techniques used to parallelize a single MD simulation have already been surveyed by several authors, 13,14 we will provide here only a brief summary of some of the salient characteristics of the three major categories of traditional approaches to this problem: (a) atom decomposition methods, (b) force decomposition methods, and (c) spatial decomposition methods.…”

Section: Standard Methods For Parallelizing MD Simulationsmentioning

confidence: 99%

A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions

2005

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Classical molecular dynamics simulations of biological macromolecules in explicitly modeled solvent typically require the evaluation of interactions between all pairs of atoms separated by no more than some distance R, with more distant interactions handled using some less expensive method. Performing such simulations for periods on the order of a millisecond is likely to require the use of massive parallelism. The extent to which such simulations can be efficiently parallelized, however, has historically been limited by the time required for interprocessor communication. This article introduces a new method for the parallel evaluation of distance-limited pairwise particle interactions that significantly reduces the amount of data transferred between processors by comparison with traditional methods. Specifically, the amount of data transferred into and out of a given processor scales as O(R 3/ 2 p Ϫ1/ 2 ), where p is the number of processors, and with constant factors that should yield a substantial performance advantage in practice.

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Section: Standard Methods For Parallelizing MD Simulationsmentioning

confidence: 99%

A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions

2005

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“…Starting from the models requiring 27 (3x3x3 set) processors or more, each processor should generally exchange messages with its 26 neighbors. Since interprocessor communication is relatively slow operation, it is more advantageous to implement the three-stage updating procedure [12] where only three pairs of exchanges are required ( Figure 3).…”

Section: Simulation Of Water Using Parallel Monte Carlo Algorithmmentioning

confidence: 99%

“…Totally 3 pairs of exchanges are needed. Adopted from [12]. It should be noted that the usage of blocking SendRecv routines of MPI library to exchange data at stage 4 plays a very important role in general synchronization of the computations over the processors set.…”

Section: Simulation Of Water Using Parallel Monte Carlo Algorithmmentioning

confidence: 99%

Parallel Monte Carlo study on caffeine-DNA interaction in aqueous solution

Kalugin

Теплухин

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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“…Several attempts have been made to parallelize Monte Carlo algorithms through system decomposition into a number of noninteracting domains. 4,5 Updating a molecule with interaction range beyond its own domain needs the most up-to-date information of its neighbor molecules from logically adjacent computers. In parallel simulations on distributed computers/clusters, communication between processors is a much slower process due to the limit of network speed and data traffic, compared with ultrafast local cache or memory operation.…”

Section: Introductionmentioning

confidence: 99%

“…The violation of strict detailed balance causes concerns about the precision of this type of parallel Monte Carlo simulation. 5 Most Monte Carlo simulations ensure strict detailed balance via random updating. Recently, we proposed a Monte Carlo algorithm based on sequential updating moves with partial randomness that only satisfies the weaker balance condition.…”

Section: Introductionmentioning

confidence: 99%

Parallel Markov chain Monte Carlo simulations

Ren

Orkoulas

2007

The Journal of Chemical Physics

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With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.

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Parallel atomistic simulations

Cited by 71 publications

References 128 publications

A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions

A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions

Parallel Monte Carlo study on caffeine-DNA interaction in aqueous solution

Parallel Markov chain Monte Carlo simulations

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