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Multithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equations

2025-02-01Artigo científico Acesso aberto
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dc.contributor.authorSilva, Bruno
dc.contributor.authorLopes, Luiz Guerreiro
dc.contributor.authorMendonça, Fábio
dc.contributor.institutionNOVALincs
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2026-03-10T12:03:01Z
dc.date.available2026-03-10T12:03:01Z
dc.date.issued2025-02-01
dc.descriptionFunding information: This work was partially supported by IntellMax–Optimization, Artificial Intelligence and Data Science, Lda., Portugal. This work was also supported by the Interactive Technologies Institute (ITI/LARSyS), funded by the Portuguese Foundation for Science and Technology (FCT) through the projects 10.54499/LA/P/0083/2020, 10.54499/UIDP/50009/2020, and 10.54499/UIDB/50009/2020. The HPC resources used in this study werec provided by the Portuguese National Distributed Computing Infrastructure (INCD) through the FCT Advanced Computing Projects 2023.09611.CPCA.A1 and 2024.07086.CPCA.A1. Publisher Copyright: © 2025 by the authors.
dc.description.abstractThis paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based parallel version of the PSO algorithm uses the inherent parallelism of modern hardware architectures. Its performance is compared against both sequential and multithreaded Central Processing Unit (CPU) implementations. The primary objective is to evaluate the efficiency and scalability of PSO across different hardware platforms with a focus on solving large-scale SNEs involving thousands of equations and variables. The GPU-parallelized and multithreaded versions of the algorithm were implemented in the Julia programming language. Performance analyses were conducted on an NVIDIA A100 GPU and an AMD EPYC 7643 CPU. The tests utilized a set of challenging, scalable SNEs with dimensions ranging from 1000 to 5000. Results demonstrate that the GPU accelerated modified PSO substantially outperforms its CPU counterparts, achieving substantial speedups and consistently surpassing the highly optimized multithreaded CPU implementation in terms of computation time and scalability as the problem size increases. Therefore, this work evaluates the trade-offs between different hardware platforms and underscores the potential of GPU-based parallelism for accelerating SNE solvers.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent35
dc.format.extent2274938
dc.identifier.doi10.3390/electronics14030584
dc.identifier.issn2079-9292
dc.identifier.otherPURE: 156583147
dc.identifier.otherPURE UUID: 28f700fc-1eea-4bd7-a207-283ecc6f6ef8
dc.identifier.otherScopus: 85217743884
dc.identifier.urihttp://hdl.handle.net/10362/201197
dc.identifier.urlhttps://www.scopus.com/pages/publications/85217743884
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso para Atribuição do Estatuto e Financiamento de Laboratórios Associados (LA)/LA%2FP%2F0083%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Programático/UIDP%2F50009%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50009%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/FCT_CPCA_2022_01/2023.09611.CPCA.A1/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/FCT_CPCA_2023_01/2024.07086.CPCA.A1/PT
dc.subjectmetaheuristic optimization
dc.subjectnonlinear equations systems
dc.subjectparallel GPU algorithms
dc.subjectswarm-based algorithms
dc.subjectControl and Systems Engineering
dc.subjectSignal Processing
dc.subjectHardware and Architecture
dc.subjectComputer Networks and Communications
dc.subjectElectrical and Electronic Engineering
dc.titleMultithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equationsen
dc.typejournal article
degois.publication.issue3
degois.publication.titleElectronics (Switzerland)
degois.publication.volume14
dspace.entity.typePublication
rcaap.rightsopenAccess

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