18,485 research outputs found
Event-Driven Molecular Dynamics in Parallel
Although event-driven algorithms have been shown to be far more efficient
than time-driven methods such as conventional molecular dynamics, they have not
become as popular. The main obstacle seems to be the difficulty of
parallelizing event-driven molecular dynamics. Several basic ideas have been
discussed in recent years, but to our knowledge no complete implementation has
been published yet. In this paper we present a parallel event-driven algorithm
including dynamic load-balancing, which can be easily implemented on any
computer architecture. To simplify matters our explanations refer to a basic
multi-particle system of hard spheres, but can be extended easily to a wide
variety of possible models.Comment: 10 pages, 9 figure
Analysis, Tracing, Characterization and Performance Modeling of Select ASCI Applications for BlueGene/L Using Parallel Discrete Event Simulation
Caltech's Jet Propulsion Laboratory (JPL) and Center for Advanced Computer Architecture (CACR) are conducting application and simulation analyses of Blue Gene/L[1] in order to establish a range of effectiveness of the architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution
An adaptive hierarchical domain decomposition method for parallel contact dynamics simulations of granular materials
A fully parallel version of the contact dynamics (CD) method is presented in
this paper. For large enough systems, 100% efficiency has been demonstrated for
up to 256 processors using a hierarchical domain decomposition with dynamic
load balancing. The iterative scheme to calculate the contact forces is left
domain-wise sequential, with data exchange after each iteration step, which
ensures its stability. The number of additional iterations required for
convergence by the partially parallel updates at the domain boundaries becomes
negligible with increasing number of particles, which allows for an effective
parallelization. Compared to the sequential implementation, we found no
influence of the parallelization on simulation results.Comment: 19 pages, 15 figures, published in Journal of Computational Physics
(2011
Reactive Programming of Simulations in Physics
We consider the Reactive Programming (RP) approach to simulate physical
systems. The choice of RP is motivated by the fact that RP genuinely offers
logical parallelism, instantaneously broadcast events, and dynamic
creation/destruction of parallel components and events. To illustrate our
approach, we consider the implementation of a system of Molecular Dynamics, in
the context of Java with the Java3D library for 3D visualisation
Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms
We present a mathematical framework for constructing and analyzing parallel
algorithms for lattice Kinetic Monte Carlo (KMC) simulations. The resulting
algorithms have the capacity to simulate a wide range of spatio-temporal scales
in spatially distributed, non-equilibrium physiochemical processes with complex
chemistry and transport micro-mechanisms. The algorithms can be tailored to
specific hierarchical parallel architectures such as multi-core processors or
clusters of Graphical Processing Units (GPUs). The proposed parallel algorithms
are controlled-error approximations of kinetic Monte Carlo algorithms,
departing from the predominant paradigm of creating parallel KMC algorithms
with exactly the same master equation as the serial one.
Our methodology relies on a spatial decomposition of the Markov operator
underlying the KMC algorithm into a hierarchy of operators corresponding to the
processors' structure in the parallel architecture. Based on this operator
decomposition, we formulate Fractional Step Approximation schemes by employing
the Trotter Theorem and its random variants; these schemes, (a) determine the
communication schedule} between processors, and (b) are run independently on
each processor through a serial KMC simulation, called a kernel, on each
fractional step time-window.
Furthermore, the proposed mathematical framework allows us to rigorously
justify the numerical and statistical consistency of the proposed algorithms,
showing the convergence of our approximating schemes to the original serial
KMC. The approach also provides a systematic evaluation of different processor
communicating schedules.Comment: 34 pages, 9 figure
A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation
The kinetic Monte Carlo (kMC) method is used in many scientific fields in
applications involving rare-event transitions. Due to its discrete stochastic
nature, efforts to parallelize kMC approaches often produce unbalanced time
evolutions requiring complex implementations to ensure correct statistics. In
the context of parallel kMC, the sequential update technique has shown promise
by generating high quality distributions with high relative efficiencies for
short-range systems. In this work, we provide an extension of the sequential
update method in a parallel context that rigorously obeys detailed balance,
which guarantees exact equilibrium statistics for all parallelization settings.
Our approach also preserves nonequilibrium dynamics with minimal error for many
parallelization settings, and can be used to achieve highly precise sampling
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