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Synchronization Overlap Trade-Off for a Model of Spatial Distribution of Species

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Abstract

Despite of the widespread implementation of agent-based models in ecological modeling and another several areas, modelers have been concerned by the time consuming of these type of models.

This paper presents a strategy to parallelize an agent-based model of spatial distribution of biological species, operating in a multi-stage synchronous distributed memory mode, as a way to obtain gains in the performance while reducing the need for synchronization. A multiprocessing implementation divides the environment (a rectangular grid corresponding to the study area) into stage-subsets, according to the number of defined or available processes. In order to ensure that there is no information loss, each stage-subset is extended with an overlapping section from each one of its neighbouring stage-subsets. The effect of the size of this overlapping on the quality of the simulations is studied. These results seem to indicate that it is possible to establish an optimal trade-off between the level of redundancy and the synchronization frequency.

The reported paralellization method was tested in a standalone multicore machine but may be seamlessly scalable to a computation cluster.

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Acknowledgements

This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, cofinanced by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. This work was also funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.

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Bioco, J., Prata, P., Cánovas, F., Fazendeiro, P. (2021). Synchronization Overlap Trade-Off for a Model of Spatial Distribution of Species. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-86960-1_21

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