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

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...

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Main Author: Victoria María Sanz
Format: Article
Language:English
Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2016-04-01
Series:Journal of Computer Science and Technology
Online Access:https://journal.info.unlp.edu.ar/JCST/article/view/510
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author Victoria María Sanz
author_facet Victoria María Sanz
author_sort Victoria María Sanz
collection DOAJ
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.
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spelling doaj.art-0caf8f066d1144e38f8b7759c5b3e8872022-12-21T17:13:42ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382016-04-0116016162243Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicoreVictoria María Sanz0School of Computer Science, National University of La Plata, ArgentinaThe 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.https://journal.info.unlp.edu.ar/JCST/article/view/510
spellingShingle Victoria María Sanz
Performance analysis and optimization of parallel Best-First Search algorithms on multicore and cluster of multicore
Journal of Computer Science and Technology
title 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_short 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
url https://journal.info.unlp.edu.ar/JCST/article/view/510
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