Relational Learning with GPUs: Accelerating Rule Coverage
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/6974 http://dx.doi.org/10.1007/s10766-015-0364-7 |
Resumo: | Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version. © 2015 Springer Science+Business Media New York |
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Relational Learning with GPUs: Accelerating Rule CoverageRelational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version. © 2015 Springer Science+Business Media New York2018-01-18T15:18:32Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6974http://dx.doi.org/10.1007/s10766-015-0364-7engAngeles,CAMWu,HInês DutraVítor Santos CostaChavez,JBinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:56Zoai:repositorio.inesctec.pt:123456789/6974Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:48.302502Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Relational Learning with GPUs: Accelerating Rule Coverage |
title |
Relational Learning with GPUs: Accelerating Rule Coverage |
spellingShingle |
Relational Learning with GPUs: Accelerating Rule Coverage Angeles,CAM |
title_short |
Relational Learning with GPUs: Accelerating Rule Coverage |
title_full |
Relational Learning with GPUs: Accelerating Rule Coverage |
title_fullStr |
Relational Learning with GPUs: Accelerating Rule Coverage |
title_full_unstemmed |
Relational Learning with GPUs: Accelerating Rule Coverage |
title_sort |
Relational Learning with GPUs: Accelerating Rule Coverage |
author |
Angeles,CAM |
author_facet |
Angeles,CAM Wu,H Inês Dutra Vítor Santos Costa Chavez,JB |
author_role |
author |
author2 |
Wu,H Inês Dutra Vítor Santos Costa Chavez,JB |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Angeles,CAM Wu,H Inês Dutra Vítor Santos Costa Chavez,JB |
description |
Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version. © 2015 Springer Science+Business Media New York |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01T00:00:00Z 2016 2018-01-18T15:18:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/6974 http://dx.doi.org/10.1007/s10766-015-0364-7 |
url |
http://repositorio.inesctec.pt/handle/123456789/6974 http://dx.doi.org/10.1007/s10766-015-0364-7 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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