Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore

Autores
Sanz, Victoria María
Año de publicación
2016
Idioma
inglés
Tipo de recurso
reseña artículo
Estado
versión publicada
Descripción
The contribution of the thesis is the development of two parallel Best-First Search algorithms, one that is suitable for execution on shared-memory machines (multicore), and another one that is suitable for execution on distributed memory machines (cluster). The former is based on the adaptation of the HDA* (Hash Distributed A*) algorithm for multicore machines proposed by (Burns et al., 2010), while the latter is based on the HDA* (Hash Distributed A*) algorithm proposed by (Kishimoto, et al., 2013). The implemented algorithms incorporate parameters and/or techniques that improve their performance, with respect to the original algorithms proposed by the authors mentioned above.
Es revisión de: http://sedici.unlp.edu.ar/handle/10915/44478
Resumen de la tesis presentada por la autora para obtener el título de Doctor en Ciencias Informáticas (UNLP, 2015).
Facultad de Informática
Materia
Ciencias Informáticas
Algorithms
Parallel algorithms
Clustering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/52388
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicoreSanz, Victoria MaríaCiencias InformáticasAlgorithmsParallel algorithmsClusteringThe contribution of the thesis is the development of two parallel Best-First Search algorithms, one that is suitable for execution on shared-memory machines (multicore), and another one that is suitable for execution on distributed memory machines (cluster). The former is based on the adaptation of the HDA* (Hash Distributed A*) algorithm for multicore machines proposed by (Burns et al., 2010), while the latter is based on the HDA* (Hash Distributed A*) algorithm proposed by (Kishimoto, et al., 2013). The implemented algorithms incorporate parameters and/or techniques that improve their performance, with respect to the original algorithms proposed by the authors mentioned above.Es revisión de: http://sedici.unlp.edu.ar/handle/10915/44478Resumen de la tesis presentada por la autora para obtener el título de Doctor en Ciencias Informáticas (UNLP, 2015).Facultad de Informática2016-04info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionRevisionhttp://purl.org/coar/resource_type/c_dcae04bcinfo:ar-repo/semantics/resenaArticuloapplication/pdf61-62http://sedici.unlp.edu.ar/handle/10915/52388enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/10/JCST-42-Thesis-Overview-2.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:04:39Zoai:sedici.unlp.edu.ar:10915/52388Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:04:40.115SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
title Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
spellingShingle Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
Sanz, Victoria María
Ciencias Informáticas
Algorithms
Parallel algorithms
Clustering
title_short Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
title_full Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
title_fullStr Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
title_full_unstemmed Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
title_sort Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
dc.creator.none.fl_str_mv Sanz, Victoria María
author Sanz, Victoria María
author_facet Sanz, Victoria María
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
Parallel algorithms
Clustering
topic Ciencias Informáticas
Algorithms
Parallel algorithms
Clustering
dc.description.none.fl_txt_mv The contribution of the thesis is the development of two parallel Best-First Search algorithms, one that is suitable for execution on shared-memory machines (multicore), and another one that is suitable for execution on distributed memory machines (cluster). The former is based on the adaptation of the HDA* (Hash Distributed A*) algorithm for multicore machines proposed by (Burns et al., 2010), while the latter is based on the HDA* (Hash Distributed A*) algorithm proposed by (Kishimoto, et al., 2013). The implemented algorithms incorporate parameters and/or techniques that improve their performance, with respect to the original algorithms proposed by the authors mentioned above.
Es revisión de: http://sedici.unlp.edu.ar/handle/10915/44478
Resumen de la tesis presentada por la autora para obtener el título de Doctor en Ciencias Informáticas (UNLP, 2015).
Facultad de Informática
description The contribution of the thesis is the development of two parallel Best-First Search algorithms, one that is suitable for execution on shared-memory machines (multicore), and another one that is suitable for execution on distributed memory machines (cluster). The former is based on the adaptation of the HDA* (Hash Distributed A*) algorithm for multicore machines proposed by (Burns et al., 2010), while the latter is based on the HDA* (Hash Distributed A*) algorithm proposed by (Kishimoto, et al., 2013). The implemented algorithms incorporate parameters and/or techniques that improve their performance, with respect to the original algorithms proposed by the authors mentioned above.
publishDate 2016
dc.date.none.fl_str_mv 2016-04
dc.type.none.fl_str_mv info:eu-repo/semantics/review
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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