Parallel stochastic simulations with rigorous distribution of pseudo-random numbers with DistMe: Application to life science simulations
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 model transformations using a model driven approach. Copyright (c) 2011 John Wiley & Sons, Ltd.
distributed stochastic simulation parallel random number generator grid and cluster computing parameter exploration