310 research outputs found
Parallelization of a Dynamic Monte Carlo Algorithm: a Partially Rejection-Free Conservative Approach
We experiment with a massively parallel implementation of an algorithm for
simulating the dynamics of metastable decay in kinetic Ising models. The
parallel scheme is directly applicable to a wide range of stochastic cellular
automata where the discrete events (updates) are Poisson arrivals. For high
performance, we utilize a continuous-time, asynchronous parallel version of the
n-fold way rejection-free algorithm. Each processing element carries an lxl
block of spins, and we employ the fast SHMEM-library routines on the Cray T3E
distributed-memory parallel architecture. Different processing elements have
different local simulated times. To ensure causality, the algorithm handles the
asynchrony in a conservative fashion. Despite relatively low utilization and an
intricate relationship between the average time increment and the size of the
spin blocks, we find that for sufficiently large l the algorithm outperforms
its corresponding parallel Metropolis (non-rejection-free) counterpart. As an
example application, we present results for metastable decay in a model
ferromagnetic or ferroelectric film, observed with a probe of area smaller than
the total system.Comment: 17 pages, 7 figures, RevTex; submitted to the Journal of
Computational Physic
An Interactive Problem Modeller and PDE Solver, Distributed on Large Scale Architectures
http://lifc.univ-fcomte.fr/dfma07/International audienceThis paper introduces a research project and a software environment to speed up and size up problem modeling with Partial Differential Equations (PDE). These PDE are defined from a \Mathematica interface, and are automatically solved by a devoted cellular automata program generated to be run on mainframes, clusters and Grids. Moreover, an interactive and graphic control of the cellular automata running allows to analyze the PDE model relevance. This environment improves large scale simulation usage in early stages of research projects
Astrobiological Complexity with Probabilistic Cellular Automata
Search for extraterrestrial life and intelligence constitutes one of the
major endeavors in science, but has yet been quantitatively modeled only rarely
and in a cursory and superficial fashion. We argue that probabilistic cellular
automata (PCA) represent the best quantitative framework for modeling
astrobiological history of the Milky Way and its Galactic Habitable Zone. The
relevant astrobiological parameters are to be modeled as the elements of the
input probability matrix for the PCA kernel. With the underlying simplicity of
the cellular automata constructs, this approach enables a quick analysis of
large and ambiguous input parameters' space. We perform a simple clustering
analysis of typical astrobiological histories and discuss the relevant boundary
conditions of practical importance for planning and guiding actual empirical
astrobiological and SETI projects. In addition to showing how the present
framework is adaptable to more complex situations and updated observational
databases from current and near-future space missions, we demonstrate how
numerical results could offer a cautious rationale for continuation of
practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo
iC2mpi: A Platform for Parallel Execution of Graph-Structured Iterative Computations
Parallelization of sequential programs is often daunting because of the substantial development cost involved. Various solutions have been proposed to address this concern, including directive-based approaches and parallelization platforms. These solutions have not always been successful, in part because many try to address all types of applications. We propose a platform for parallelization of a class of applications that have similar computational structure, namely graph-structured iterative applications. iC2mpi is a unique proof-of-concept prototype platform that provides relatively easy parallelization of existing sequential programs and facilitates experimentation with static partitioning and dynamic load balancing schemes. We demonstrate with various generic application graph topologies and an existing application, namely a time-stepped battlefield management simulation, that our platform can produce good performance with very little effort
Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
BACKGROUND: Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. METHODS: We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. RESULTS: We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. CONCLUSIONS: Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
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