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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/42387
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:01:23Zoai: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:13292025-09-29 11:01:24.053SEDICI (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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/42387 |
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dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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