Parallelization strategies for high-performance and energy-efficient epidemic spread simulations

Simulation analysis of epidemic disease spread is crucial for a proper social and governmental response. Certain susceptible–infected–recovered (SIR) models based on cellular automata (CA) have proven to be effective tools for this purpose. Despite the growing interest in these simulation models, fe...

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Autores: Cagigas Muñiz, Daniel, Díaz del Río, Fernando, Sevillano Ramos, José Luis, Guisado Lizar, José Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/177989
Acceso en línea:https://hdl.handle.net/11441/177989
https://doi.org/10.1016/j.simpat.2024.103059
Access Level:acceso abierto
Palabra clave:Epidemic spread
Cellular automata
Parallel computing
Covid-19
Green computing
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spelling Parallelization strategies for high-performance and energy-efficient epidemic spread simulationsCagigas Muñiz, DanielDíaz del Río, FernandoSevillano Ramos, José LuisGuisado Lizar, José LuisEpidemic spreadCellular automataParallel computingCovid-19Green computingSimulation analysis of epidemic disease spread is crucial for a proper social and governmental response. Certain susceptible–infected–recovered (SIR) models based on cellular automata (CA) have proven to be effective tools for this purpose. Despite the growing interest in these simulation models, few studies have addressed computational efficiency. Many models are not parallelized and, as a result, are computationally inefficient. Moreover, computational efficiency is often solely associated with runtime, with limited attention given to energy consumption and energy-efficient software implementations. This paper presents various parallel implementations of a successful Covid-19 cellular automaton SIR model on multiprocessors and Graphics Processing Units (GPUs), significantly improving the performance of existing codes while substantially reducing energy consumption. The performance analysis of these parallel implementations demonstrates that simulations can be reduced from hours to under a second, with energy consumption reduced by more than three orders of magnitude. Additionally, the results reveal that in cases where multiple parallel multiprocessor alternatives are available, there is not always a direct correlation between the shortest execution time and the lowest energy consumption in CA simulations. This work aims to support practitioners interested in implementing or utilizing parallel, energy-efficient SIR model simulations for future epidemic outbreaks, green computing initiatives, and efficient cellular automata simulations in general.Elsevier Science BVArquitectura y Tecnología de ComputadoresMinisterio de Ciencia e Innovación (MICIN). EspañaAgencia Estatal de Investigación. España2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/177989https://doi.org/10.1016/j.simpat.2024.103059reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSimulation Modelling Practice and Theory, 140, 103059.PID2023-147795NA-I00PID2023-151065OB-I00TED2021-130825B-I00https://www.sciencedirect.com/science/article/pii/S1569190X24001734?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1779892026-06-17T12:51:07Z
dc.title.none.fl_str_mv Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
title Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
spellingShingle Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
Cagigas Muñiz, Daniel
Epidemic spread
Cellular automata
Parallel computing
Covid-19
Green computing
title_short Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
title_full Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
title_fullStr Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
title_full_unstemmed Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
title_sort Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
dc.creator.none.fl_str_mv Cagigas Muñiz, Daniel
Díaz del Río, Fernando
Sevillano Ramos, José Luis
Guisado Lizar, José Luis
author Cagigas Muñiz, Daniel
author_facet Cagigas Muñiz, Daniel
Díaz del Río, Fernando
Sevillano Ramos, José Luis
Guisado Lizar, José Luis
author_role author
author2 Díaz del Río, Fernando
Sevillano Ramos, José Luis
Guisado Lizar, José Luis
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
Ministerio de Ciencia e Innovación (MICIN). España
Agencia Estatal de Investigación. España
dc.subject.none.fl_str_mv Epidemic spread
Cellular automata
Parallel computing
Covid-19
Green computing
topic Epidemic spread
Cellular automata
Parallel computing
Covid-19
Green computing
description Simulation analysis of epidemic disease spread is crucial for a proper social and governmental response. Certain susceptible–infected–recovered (SIR) models based on cellular automata (CA) have proven to be effective tools for this purpose. Despite the growing interest in these simulation models, few studies have addressed computational efficiency. Many models are not parallelized and, as a result, are computationally inefficient. Moreover, computational efficiency is often solely associated with runtime, with limited attention given to energy consumption and energy-efficient software implementations. This paper presents various parallel implementations of a successful Covid-19 cellular automaton SIR model on multiprocessors and Graphics Processing Units (GPUs), significantly improving the performance of existing codes while substantially reducing energy consumption. The performance analysis of these parallel implementations demonstrates that simulations can be reduced from hours to under a second, with energy consumption reduced by more than three orders of magnitude. Additionally, the results reveal that in cases where multiple parallel multiprocessor alternatives are available, there is not always a direct correlation between the shortest execution time and the lowest energy consumption in CA simulations. This work aims to support practitioners interested in implementing or utilizing parallel, energy-efficient SIR model simulations for future epidemic outbreaks, green computing initiatives, and efficient cellular automata simulations in general.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/177989
https://doi.org/10.1016/j.simpat.2024.103059
url https://hdl.handle.net/11441/177989
https://doi.org/10.1016/j.simpat.2024.103059
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Simulation Modelling Practice and Theory, 140, 103059.
PID2023-147795NA-I00
PID2023-151065OB-I00
TED2021-130825B-I00
https://www.sciencedirect.com/science/article/pii/S1569190X24001734?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science BV
publisher.none.fl_str_mv Elsevier Science BV
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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