Implementing Cloud-based Parallel Metaheuristics: an Overview

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Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enoug...

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Autores principales: González, Patricia, Pardo, Xoán C., Doallo, Ramón, Banga, Julio R.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Ciencias Informáticas
cloud computing
MapReduce
MPI
parallel metaheuristics
computacin en la nube
Spark
metaheursticas paralelas
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/71658
http://journal.info.unlp.edu.ar/JCST/article/view/1109/911
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-71658
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
cloud computing
MapReduce
MPI
parallel metaheuristics
computacin en la nube
Spark
metaheursticas paralelas
spellingShingle Ciencias Informáticas
cloud computing
MapReduce
MPI
parallel metaheuristics
computacin en la nube
Spark
metaheursticas paralelas
González, Patricia
Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
Implementing Cloud-based Parallel Metaheuristics: an Overview
topic_facet Ciencias Informáticas
cloud computing
MapReduce
MPI
parallel metaheuristics
computacin en la nube
Spark
metaheursticas paralelas
description Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel implementation applying HPC techniques is a common approach for efficiently using available resources to reduce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuristics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.
format Articulo
Articulo
author González, Patricia
Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
author_facet González, Patricia
Pardo, Xoán C.
Doallo, Ramón
Banga, Julio R.
author_sort González, Patricia
title Implementing Cloud-based Parallel Metaheuristics: an Overview
title_short Implementing Cloud-based Parallel Metaheuristics: an Overview
title_full Implementing Cloud-based Parallel Metaheuristics: an Overview
title_fullStr Implementing Cloud-based Parallel Metaheuristics: an Overview
title_full_unstemmed Implementing Cloud-based Parallel Metaheuristics: an Overview
title_sort implementing cloud-based parallel metaheuristics: an overview
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/71658
http://journal.info.unlp.edu.ar/JCST/article/view/1109/911
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