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World-Wide Distributed multiple replications in parallel for Quantitative Sequential Simulation

World-Wide Distributed Multiple Replications in Parallel for...

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11th International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Haque, Mofassir Pawlikowski, Krzysztof McNickle, Don Ewing, Gregory Univ Canterbury Dept Comp Sci Christchurch 8140 New Zealand Univ Canterbury Dept Management Christchurch 8140 New Zealand

ISBN: (纸本)9783642246685

With the recent deployment of global experimental networking facilities, dozens of computer networks with large numbers of computers have become available for scientific studies. multiple replications in parallel (MRIP) is a distributed scenario of sequential quantitative stochastic simulation which offers significant speedup of simulation if it is executed on multiple computers of a local area network. We report results of running MRIP simulations on PlanetLab, a global overlay network which can currently access more than a thousand computers in forty different countries round the globe. Our simulations were run using Akaroa2, a universal controller of quantitative discrete event simulation designed for automatic launching of MRIP-based experiments. Our experimental results provide strong evidence that global experimental networks, such as PlanetLab, can efficiently be used for quantitative simulation, without compromising speed and efficiency.

关键词： multiple replications in parallel Experimental networking facilities Akaroa2 PlanetLab Sequential quantitative stochastic simulation Open queuing network

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AKAROA2: A CONTROLLER OF DISCRETE-EVENT SIMULATION WHICH EXPLOITS THE DISTRIBUTED COMPUTING RESOURCES OF NETWORKS

AKAROA2: A CONTROLLER OF DISCRETE-EVENT SIMULATION WHICH EXP...

引用

24th European Conference on Modelling and Simulation

作者： McNickle, Don Pawlikowski, Krzysztof Ewing, Greg Univ Canterbury Dept Management Private Bag 4800 Christchurch New Zealand Univ Canterbury Comp Sci & Software Engn Christchurch New Zealand

ISBN: (纸本)9780956494405

This paper describes and summarises our research on enhancing the methodology of automated discrete-event simulation and its implementation in Akaroa2, a controller of such simulation studies. Akaroa2 addresses two major practical issues in the application of stochastic simulation in performance evaluation studies of complex dynamic systems: (i) accuracy of the final results;and (ii) the length of time required to achieve these results. (i) is addressed by running simulations sequentially, with on-line analysis of statistical errors until these reach an acceptably low level. For (ii), Akaroa2 launches multiple copies of a simulation program on networked processors, applying the multiple replications in parallel (MRIP) scenario. In MRIP the processors run independent replications, generating statistically equivalent streams of simulation output data. These data are fed to a global data analyser responsible for analysis of the results and for stopping the simulation. We outline main design issues of Akaroa2, and detail some of the improvements and extensions to this tool over the last 10 years.

关键词： Discrete-event simulation sequential simulation statistical analysis multiple replications in parallel

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DistMe: A generic toolkit for stochastic simulation distribution

DistMe: A generic toolkit for stochastic simulation distribu...

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20th European Simulation and Modelling Conference (ESM 2006)

作者： Reuillon, Romain Hill, David R. C. Blaise Pascal Univ ISIMA LIMOS CNRSUMR 6158 BP 10125Campus Cezeaux F-63173 Aubiere France

ISBN: (纸本)9789077381304

Stochastic simulations are considered as naturally parallel, because many replications of the same experiment may be distributed on multiple execution units to reduce the global simulation time. However, one need to take care of the underling random number streams and ensure the lack of intra and inter stream correlations that can lead to erroneous results. Based on generic random number generator statuses formats and automatic tools to distributed stochastic simulations, DistMe is a java toolkit fully usable to speed up Monte Carlo simulations using any parallel machine based on the bag of work paradigm. A nuclear physic application and an environmental simulation software have been parallelized with this toolkit and results are very convincing.

关键词： distributed stochastic simulation multiple replications in parallel parallel random number generator

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