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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/52388
Ver los metadatos del registro completo
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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:UNLP2025-09-03T10:37:18Zoai: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:13292025-09-03 10:37:18.618SEDICI (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 Revision http://purl.org/coar/resource_type/c_dcae04bc info:ar-repo/semantics/resenaArticulo |
format |
review |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/52388 |
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http://sedici.unlp.edu.ar/handle/10915/52388 |
dc.language.none.fl_str_mv |
eng |
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eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 61-62 |
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