Publicação
GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming
| Resumo: | Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1, 000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems. |
|---|---|
| Autores principais: | Trujillo, Leonardo |
| Outros Autores: | Muñoz Contreras, Jose Manuel; Hernandez, Daniel E.; Castelli, Mauro; Tapia, Juan J. |
| Assunto: | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868983724115230720 |
|---|---|
| author | Trujillo, Leonardo |
| author2 | Muñoz Contreras, Jose Manuel Hernandez, Daniel E. Castelli, Mauro Tapia, Juan J. |
| author2_role | author author author author |
| author_facet | Trujillo, Leonardo Muñoz Contreras, Jose Manuel Hernandez, Daniel E. Castelli, Mauro Tapia, Juan J. |
| author_role | author |
| contributor_name_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier BV RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Trujillo, Leonardo\"},{\"Person.name\":\"Muñoz Contreras, Jose Manuel\"},{\"Person.name\":\"Hernandez, Daniel E.\"},{\"Person.name\":\"Castelli, Mauro\"},{\"Person.name\":\"Tapia, Juan J.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier BV RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Trujillo, Leonardo Muñoz Contreras, Jose Manuel Hernandez, Daniel E. Castelli, Mauro Tapia, Juan J. |
| datacite.date.Accepted.fl_str_mv | 2022-06-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-05-18T22:47:45Z |
| datacite.date.embargoed.fl_str_mv | 2022-05-18T22:47:45Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| datacite.titles.title.fl_str_mv | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| dc.contributor.none.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School Elsevier BV RUN |
| dc.creator.none.fl_str_mv | Trujillo, Leonardo Muñoz Contreras, Jose Manuel Hernandez, Daniel E. Castelli, Mauro Tapia, Juan J. |
| dc.date.Accepted.fl_str_mv | 2022-06-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-05-18T22:47:45Z |
| dc.date.embargoed.fl_str_mv | 2022-05-18T22:47:45Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/138207 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| dc.title.fl_str_mv | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1, 000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/f03988cc-fc88-4847-8473-22b4a82c4b03/download |
| funder_facet_str_mv | FCT{{{_:::_}}}Fundação para a Ciência e a Tecnologia |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 3599-PPCDT |
| id | run_d344ec33eeb201a24c19a4bb3f5d8578 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/138207 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/138207 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Trujillo, Leonardo Muñoz Contreras, Jose Manuel Hernandez, Daniel E. Castelli, Mauro Tapia, Juan J. |
| publishDate | 2022 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenGeometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1, 000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.application/pdfenGSGP-CUDA: A CUDA framework for Geometric Semantic Genetic ProgrammingTrujillo, LeonardoMuñoz Contreras, Jose ManuelHernandez, Daniel E.Castelli, MauroTapia, Juan J.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolElsevier BVHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNIsPartOfPURE: 43986256URNIsPartOfPURE UUID: f45ab250-dc7f-46fe-8049-600e5a6b0f7aURNIsPartOfcrossref: 10.1016/j.softx.2022.101085URNIsPartOfScopus: 85129991202URNIsPartOfWOS: 000800216100003URNIsPartOfORCID: /0000-0002-8793-1451/work/113263202DOIIsPartOf10.1016/j.softx.2022.1010852022-05-18T22:47:45Z2022-06-012022-06-01T00:00:00ZHandlehttp://hdl.handle.net/10362/138207http://purl.org/coar/access_right/c_abf2open accessGenetic ProgrammingGeometric Semantic Genetic ProgrammingCUDAGPUSoftwareComputer Science Applications380112 bytesFundação para a Ciência e a TecnologiaData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal3599-PPCDTCrossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/f03988cc-fc88-4847-8473-22b4a82c4b03/download |
| spellingShingle | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming Trujillo, Leonardo Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| status | SINGLETON |
| subject.fl_str_mv | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| title | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| title_full | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| title_fullStr | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| title_full_unstemmed | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| title_short | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| title_sort | GSGP-CUDA: A CUDA framework for Geometric Semantic Genetic Programming |
| topic | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| topic_facet | Genetic Programming Geometric Semantic Genetic Programming CUDA GPU Software Computer Science Applications |
| url | http://hdl.handle.net/10362/138207 |
| visible | 1 |