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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2016
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| 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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| _version_ | 1864513459490652160 |
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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 |
| repository.mail.fl_str_mv | |
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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 |