Properties of simulated annealing and genetic algorithms for mapping data to multicomputers

We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. These properties include sensitiviiy to user parameters, fault tolerance capability, and applicability to different multicomputer topologies. Some user par...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Mansour, Nashat (author)
مؤلفون آخرون: Kawash, Jalal (author), Diab, Hassan (author)
التنسيق: article
منشور في: 1997
الوصول للمادة أونلاين:http://hdl.handle.net/10725/2962
http://dx.doi.org/10.1080/01495739708941426
http://www.tandfonline.com/doi/abs/10.1080/01495739708941426
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author Mansour, Nashat
author2 Kawash, Jalal
Diab, Hassan
author2_role author
author
author_facet Mansour, Nashat
Kawash, Jalal
Diab, Hassan
author_role author
dc.creator.none.fl_str_mv Mansour, Nashat
Kawash, Jalal
Diab, Hassan
dc.date.none.fl_str_mv 1997
2016-01-26T13:32:02Z
2016-01-26T13:32:02Z
2016-01-26
dc.identifier.none.fl_str_mv 1063-7192
http://hdl.handle.net/10725/2962
http://dx.doi.org/10.1080/01495739708941426
Mansour, N., Kawash, J., & Diab, H. (1997). Properties of simulated annealing and genetic algorithms for mapping data to multicomputers. Parallel Algorithms and Applications, 12(4), 279-296.
http://www.tandfonline.com/doi/abs/10.1080/01495739708941426
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv Parallel Algorithms and Applications
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.title.none.fl_str_mv Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
dc.type.none.fl_str_mv Article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. These properties include sensitiviiy to user parameters, fault tolerance capability, and applicability to different multicomputer topologies. Some user parameters are included in the objective function and are architecture- or problem-dependent parameters. The others are used in the GA and SA algorithms. The fault tolerance capability is demonstrated by mapping data to a multicomputer with some faulty processors. We assume a hypercube multicomputer architecture in most experiments. However, mapping to mesh, array, ring, tree, and star graph topologies is also demonstrated. The experimental results show that the GA and SA are insensitive to user parameters in wide ranges, completely fault tolerant, and unbiased towards particular multicomputer topologies. These properties of flexibility and general applicability, which are lacking in other heuristic algorithms, make the GA and SA attractive for automatic parallelization systems.
eu_rights_str_mv openAccess
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id LAURepo_c8613c35c7b3ce9c779a4fa68481d89f
identifier_str_mv 1063-7192
Mansour, N., Kawash, J., & Diab, H. (1997). Properties of simulated annealing and genetic algorithms for mapping data to multicomputers. Parallel Algorithms and Applications, 12(4), 279-296.
language_invalid_str_mv en
network_acronym_str LAURepo
network_name_str Lebanese American University repository
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spelling Properties of simulated annealing and genetic algorithms for mapping data to multicomputersMansour, NashatKawash, JalalDiab, HassanWe experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. These properties include sensitiviiy to user parameters, fault tolerance capability, and applicability to different multicomputer topologies. Some user parameters are included in the objective function and are architecture- or problem-dependent parameters. The others are used in the GA and SA algorithms. The fault tolerance capability is demonstrated by mapping data to a multicomputer with some faulty processors. We assume a hypercube multicomputer architecture in most experiments. However, mapping to mesh, array, ring, tree, and star graph topologies is also demonstrated. The experimental results show that the GA and SA are insensitive to user parameters in wide ranges, completely fault tolerant, and unbiased towards particular multicomputer topologies. These properties of flexibility and general applicability, which are lacking in other heuristic algorithms, make the GA and SA attractive for automatic parallelization systems.PublishedN/A2016-01-26T13:32:02Z2016-01-26T13:32:02Z19972016-01-26Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1063-7192http://hdl.handle.net/10725/2962http://dx.doi.org/10.1080/01495739708941426Mansour, N., Kawash, J., & Diab, H. (1997). Properties of simulated annealing and genetic algorithms for mapping data to multicomputers. Parallel Algorithms and Applications, 12(4), 279-296.http://www.tandfonline.com/doi/abs/10.1080/01495739708941426enParallel Algorithms and Applicationsinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/29622016-08-01T10:13:15Z
spellingShingle Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
Mansour, Nashat
status_str publishedVersion
title Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
title_full Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
title_fullStr Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
title_full_unstemmed Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
title_short Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
title_sort Properties of simulated annealing and genetic algorithms for mapping data to multicomputers
url http://hdl.handle.net/10725/2962
http://dx.doi.org/10.1080/01495739708941426
http://www.tandfonline.com/doi/abs/10.1080/01495739708941426