Approaching parallel computing to simulating population dynamics in demography

Authored by Cristina Montanola-Sales, Bhakti S S Onggo, Josep Casanovas-Garcia, Jose Maria Cela-Espin, Adriana Kaplan-Marcusan

Date Published: 2016

DOI: 10.1016/j.parco.2016.07.001

Sponsors: Research and Innovation Ministry of the Spanish Government Secreteria d'Universitats i Recerca de la Generalitat de Catalunya

Platforms: C++ Yades

Model Documentation: UML Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model. (C) 2016 Elsevier B.V. All rights reserved.

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