E. Mosca, I. Merelli, L. Milanesi, A. Clematis, D. D'Agostino
{"title":"A Parallel Implementation of the Stau-DPP Stochastic Simulator for the Modelling of Biological Systems","authors":"E. Mosca, I. Merelli, L. Milanesi, A. Clematis, D. D'Agostino","doi":"10.1109/PDP.2013.68","DOIUrl":null,"url":null,"abstract":"In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. The execution of thousands of simulation can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In this work we discuss the different strategies that can be implemented for the parallelization of a space aware τ-DPP variant, that is proving a C-MPI implementation of the system and discussing its performances according to the simulation of a particle diffusion in a crowded environment.","PeriodicalId":202977,"journal":{"name":"2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDP.2013.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. The execution of thousands of simulation can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In this work we discuss the different strategies that can be implemented for the parallelization of a space aware τ-DPP variant, that is proving a C-MPI implementation of the system and discussing its performances according to the simulation of a particle diffusion in a crowded environment.