Parallelization strategies for spatial agent-based models

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dc.contributor.authorFachada, Nuno
dc.contributor.authorLopes, Vitor V.
dc.contributor.authorMartins, Rui C.
dc.contributor.authorRosa, Agostinho C.
dc.contributor.institutionEscola de Comunicação, Arquitetura, Artes e Tecnologias da Informação
dc.date.issued2017
dc.descriptionInternational Journal of Parallel Programming
dc.description.abstractAgent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: (1) compare the performance of this implementation with an existing NetLogo implementation; and, (2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: (1) model parallelization can yield considerable performance gains; (2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, (3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.en
dc.description.statusNon peer reviewed
dc.formatapplication/pdf
dc.identifier.citationFachada , N , Lopes , V V , Martins , R C & Rosa , A C 2017 , ' Parallelization strategies for spatial agent-based models ' , International Journal of Parallel Programming .
dc.identifier.issn1573-7640
dc.language.isoeng
dc.publisherSpringer New York
dc.relation.ispartofInternational Journal of Parallel Programming
dc.rightsopenAccess
dc.subjectMODELAÇÃO BASEADA EM AGENTES
dc.subjectMEMÓRIA COMPARTILHADA
dc.subjectMULTISSEGMENTAÇÃO
dc.subjectMULTITHREADING
dc.subjectAGENT-BASED MODELING
dc.subjectSHARED MEMORY
dc.titleParallelization strategies for spatial agent-based modelsen
dc.typearticle

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