Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case

Autores
Montiel, Santiago; Guala, Sebastian
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Simulations of agent-based models developed for topics of learning and inductive reasoning in artificial intelligence, social behavior, decision making, etc., are progressively requiring higher power processes while they increase their participation as management and political decisions support. In this work we develop the implementation of the Minority Game Model for HPC platforms in order to analyze the performance of simulations related to contexts of agent-based models for large scales. We compare times to parallel and sequential processes for several instances and get the corresponding speedup. For this work we use the MPI system with a hardware configuration of Master-Worker (Slave) paradigm with a cluster of upto 10 processors as workers. In order to improve efficiency, we evaluate performances for several sizes of clusters varying the size of the instances of the problem and detect optimum configurations for some instances of simulation.
Eje: XIV Workshop de Procesamiento Distribuido y Paralelo
Red de Universidades con Carreras de Informática (RedUNCI)
Materia
Ciencias Informáticas
HPC
minority game
ARTIFICIAL INTELLIGENCE
simulations of complex system
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42387
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/42387
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network_name_str SEDICI (UNLP)
spelling Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game CaseMontiel, SantiagoGuala, SebastianCiencias InformáticasHPCminority gameARTIFICIAL INTELLIGENCEsimulations of complex systemSimulations of agent-based models developed for topics of learning and inductive reasoning in artificial intelligence, social behavior, decision making, etc., are progressively requiring higher power processes while they increase their participation as management and political decisions support. In this work we develop the implementation of the Minority Game Model for HPC platforms in order to analyze the performance of simulations related to contexts of agent-based models for large scales. We compare times to parallel and sequential processes for several instances and get the corresponding speedup. For this work we use the MPI system with a hardware configuration of Master-Worker (Slave) paradigm with a cluster of upto 10 processors as workers. In order to improve efficiency, we evaluate performances for several sizes of clusters varying the size of the instances of the problem and detect optimum configurations for some instances of simulation.Eje: XIV Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras de Informática (RedUNCI)2014-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/42387enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:42:55Zoai:sedici.unlp.edu.ar:10915/42387Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:42:56.195SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
title Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
spellingShingle Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
Montiel, Santiago
Ciencias Informáticas
HPC
minority game
ARTIFICIAL INTELLIGENCE
simulations of complex system
title_short Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
title_full Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
title_fullStr Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
title_full_unstemmed Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
title_sort Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case
dc.creator.none.fl_str_mv Montiel, Santiago
Guala, Sebastian
author Montiel, Santiago
author_facet Montiel, Santiago
Guala, Sebastian
author_role author
author2 Guala, Sebastian
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
HPC
minority game
ARTIFICIAL INTELLIGENCE
simulations of complex system
topic Ciencias Informáticas
HPC
minority game
ARTIFICIAL INTELLIGENCE
simulations of complex system
dc.description.none.fl_txt_mv Simulations of agent-based models developed for topics of learning and inductive reasoning in artificial intelligence, social behavior, decision making, etc., are progressively requiring higher power processes while they increase their participation as management and political decisions support. In this work we develop the implementation of the Minority Game Model for HPC platforms in order to analyze the performance of simulations related to contexts of agent-based models for large scales. We compare times to parallel and sequential processes for several instances and get the corresponding speedup. For this work we use the MPI system with a hardware configuration of Master-Worker (Slave) paradigm with a cluster of upto 10 processors as workers. In order to improve efficiency, we evaluate performances for several sizes of clusters varying the size of the instances of the problem and detect optimum configurations for some instances of simulation.
Eje: XIV Workshop de Procesamiento Distribuido y Paralelo
Red de Universidades con Carreras de Informática (RedUNCI)
description Simulations of agent-based models developed for topics of learning and inductive reasoning in artificial intelligence, social behavior, decision making, etc., are progressively requiring higher power processes while they increase their participation as management and political decisions support. In this work we develop the implementation of the Minority Game Model for HPC platforms in order to analyze the performance of simulations related to contexts of agent-based models for large scales. We compare times to parallel and sequential processes for several instances and get the corresponding speedup. For this work we use the MPI system with a hardware configuration of Master-Worker (Slave) paradigm with a cluster of upto 10 processors as workers. In order to improve efficiency, we evaluate performances for several sizes of clusters varying the size of the instances of the problem and detect optimum configurations for some instances of simulation.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
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url http://sedici.unlp.edu.ar/handle/10915/42387
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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