Allocating data to distributed-memory multiprocessors by genetic algorithms

We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on...

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Main Author: Mansour, Nashat (author)
Other Authors: Fox, Geoffrey C. (author)
Format: article
Published: 2016
Online Access:http://hdl.handle.net/10725/2947
http://dx.doi.org/10.1002/cpe.4330060602
http://onlinelibrary.wiley.com/doi/10.1002/cpe.4330060602/full
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author Mansour, Nashat
author2 Fox, Geoffrey C.
author2_role author
author_facet Mansour, Nashat
Fox, Geoffrey C.
author_role author
dc.creator.none.fl_str_mv Mansour, Nashat
Fox, Geoffrey C.
dc.date.none.fl_str_mv 2016-01-25T13:24:37Z
2016-01-25T13:24:37Z
2016-01-25
dc.identifier.none.fl_str_mv 1532-0626
http://hdl.handle.net/10725/2947
http://dx.doi.org/10.1002/cpe.4330060602
Mansour, N., & Fox, G. C. (1994). Allocating data to distributed‐memory multiprocessors by genetic algorithms. Concurrency: Practice and Experience, 6(6), 485-504.
http://onlinelibrary.wiley.com/doi/10.1002/cpe.4330060602/full
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv Concurrency and computation
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.title.none.fl_str_mv Allocating data to distributed-memory multiprocessors by genetic algorithms
dc.type.none.fl_str_mv Article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.
eu_rights_str_mv openAccess
format article
id LAURepo_9f5dfbccc79f978e3a3e5e2ce5817aea
identifier_str_mv 1532-0626
Mansour, N., & Fox, G. C. (1994). Allocating data to distributed‐memory multiprocessors by genetic algorithms. Concurrency: Practice and Experience, 6(6), 485-504.
language_invalid_str_mv en
network_acronym_str LAURepo
network_name_str Lebanese American University repository
oai_identifier_str oai:laur.lau.edu.lb:10725/2947
publishDate 2016
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spelling Allocating data to distributed-memory multiprocessors by genetic algorithmsMansour, NashatFox, Geoffrey C.We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.PublishedN/A2016-01-25T13:24:37Z2016-01-25T13:24:37Z2016-01-25Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1532-0626http://hdl.handle.net/10725/2947http://dx.doi.org/10.1002/cpe.4330060602Mansour, N., & Fox, G. C. (1994). Allocating data to distributed‐memory multiprocessors by genetic algorithms. Concurrency: Practice and Experience, 6(6), 485-504.http://onlinelibrary.wiley.com/doi/10.1002/cpe.4330060602/fullenConcurrency and computationinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/29472017-04-12T13:20:37Z
spellingShingle Allocating data to distributed-memory multiprocessors by genetic algorithms
Mansour, Nashat
status_str publishedVersion
title Allocating data to distributed-memory multiprocessors by genetic algorithms
title_full Allocating data to distributed-memory multiprocessors by genetic algorithms
title_fullStr Allocating data to distributed-memory multiprocessors by genetic algorithms
title_full_unstemmed Allocating data to distributed-memory multiprocessors by genetic algorithms
title_short Allocating data to distributed-memory multiprocessors by genetic algorithms
title_sort Allocating data to distributed-memory multiprocessors by genetic algorithms
url http://hdl.handle.net/10725/2947
http://dx.doi.org/10.1002/cpe.4330060602
http://onlinelibrary.wiley.com/doi/10.1002/cpe.4330060602/full