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Parallel stochastic simulations with rigorous distribution of pseudo-random numbers with DistMe: Application to life science simulations

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2012年第7期24卷 723-738页

作者： Reuillon, Romain Traore, Mamadou K. Passerat-Palmbach, Jonathan Hill, David R. C. Clermont Univ LIMOS F-63000 Clermont Ferrand France Univ Blaise Pascal LIMOS F-63000 Clermont Ferrand France CNRS French Natl Ctr Res UMR LIMOS 6158 F-63173 Aubiere France ISIMA F-63173 Aubiere France

This paper presents an open source toolkit allowing a rigorous distribution of stochastic simulations. It is designed according to the state of the art in pseudo-random numbers partitioning techniques. Based on a generic XML format for saving pseudo-random number generator states, each state contains adapted metadata. This toolkit named DistMe is usable by modelers who are non-specialists in parallelizing stochastic simulations, it helps in distributing the replications and in the generation of experimental plans. It automatically writes ready for runtime scripts for various parallel platforms, encapsulating the burden linked to the management of status files for different pseudo-random generators. The automation of this task avoids many human mistakes. The toolkit has been designed based on a model driven engineering approach: the user builds a model of its simulation and the toolkit helps in distributing independent stochastic experiments. In this paper, the toolkit architecture is exposed, and two examples in life science research domains are detailed. The preliminary design of the DistMe toolkit was achieved when dealing with the distribution of a nuclear medicine application using the largest European computing grid: European Grid Initiative (EGI). Thanks to our alpha version of the software toolbox, the equivalent of 3?years of computing was achieved in a few days. Next, we present the second application in another domain to show the potential and genericity of the DistMe toolkit. A small experimental plan with 1024 distributed stochastic experiments was run on a local computing cluster to explore scenarios of an environmental application. For both applications, the proposed toolkit was able to automatically generate distribution scripts with independent pseudo-random number streams, and it also automatically parameterized the simulation input files to follow an experimental design. The automatic generation of scripts and input files is achieved, thanks to mod

关键词： distributed stochastic simulation parallel random number generator grid and cluster computing parameter exploration

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Rigorous distribution of stochastic simulations using the DistMe toolkit

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2008年第1期55卷 595-603页

作者： Reuillon, R. Hill, D. R. C. El Bitar, Z. Breton, V. CNRS LIMOS UMR 6158 Lab Comp Sci Modeling & Syst Optimizat F-63177 Aubiere France Lab Particle Phys F-63000 Clermont Ferrand France

Monte Carlo simulations are considered as naturally parallel, because many replications of the same experiment can be distributed on multiple execution units to reduce the global simulation time. However, one needs to take care of the underlying random number streams and ensure that the generated streams do not show intra or inter-correlations. Such errors occur in naive parallelizing approaches, they can lead to erroneous results or to a significant loss in precision. Based on a generic and documented XML format for random number generator statuses and on automatic tools to distribute stochastic simulations, the DistMe software package eases the distribution of stochastic simulations, while keeping the quality of the parallel random number streams as a critical issue. It is written in Java and has been designed to be run on any operating system and hardware with a Java virtual machine available. It has been designed using model engineering to obtain a high quality, modular and very extensible software. This toolkit, freely available on Sourceforge, is designed to speed up Monte Carlo simulations using any parallel machine based on the bag of work paradigm. It provides the user with a set of classes representing a description at a meta level of his simulation environments. Once the developer has described his simulation using DistMe classes, simulation jobs ready for runtime are instantiated. This software is released under GPL licence and the latest development sources are available online (Sourceforge CVS). This paper presents the architecture of DistMe and simulation distribution examples for Geant4 and GATE simulations. The impact of correlations is shown on the GATE application.

关键词： distributed stochastic simulation GATE geant4 Monte Carlo multiple replication in parallel

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PARMONC - A Software Library for Massively Parallel stochastic simulation

PARMONC - A Software Library for Massively Parallel Stochast...

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11th International Conference on Parallel Computing Technologies

作者： Marchenko, Mikhail Inst Computat Math & Math Geophys SB RAS Novosibirsk 630090 Russia

ISBN: (纸本)9783642231773

In this paper, the software library PARMONC that was developed for the massively parallel simulation by Monte Carlo method on supercomputers is presented. The "core" of the library is a well tested, fast and reliable long-period parallel random numbers generator. Routines from the PARMONC can be called in the user-supplied programs written in C, C++ or in FORTRAN without explicit usage of MPI instructions. Routines from the PARMONC automatically calculate sample means of interest and the corresponding computation errors. A computational load is automatically distributed among processors in an optimal way. The routines enable resuming the simulation that was previously performed and automatically take into account its results. The PARMONC is implemented on high-performance clusters of the Siberian Supercomputer Center.

关键词： Monte Carlo method distributed stochastic simulation random number generator parallel computation supercomputers

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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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