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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| Formato: | Articulo Revision |
| Lenguaje: | Inglés |
| Publicado: |
2016
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/52388 http://journal.info.unlp.edu.ar/wp-content/uploads/2015/10/JCST-42-Thesis-Overview-2.pdf |
| Aporte de: |
| Sumario: | 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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