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CUDA-JMI: Acceleration of feature selection on heterogeneous systems
dc.contributor.author | González-Domínguez, Jorge | |
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.date.accessioned | 2023-12-15T13:38:37Z | |
dc.date.available | 2023-12-15T13:38:37Z | |
dc.date.issued | 2020-01 | |
dc.identifier.citation | J. González-Domínguez, R. R. Expósito, and V. Bolón-Canedo, "CUDA-JMI: Acceleration of feature selection on heterogeneous systems", Future Generation Computer Systems, Vol. 102, pp. 426-436, Jan. 2020, https://doi.org/10.1016/j.future.2019.08.031 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34521 | |
dc.description | ©2019 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Future Generation Computer Systems. The Version of Record is available online at https://doi.org/10.1016/j.future.2019.08.031 | es_ES |
dc.description | Versión final aceptada de: J. González-Domínguez, R. R. Expósito, and V. Bolón-Canedo, "CUDA-JMI: Acceleration of feature selection on heterogeneous systemss", Future Generation Computer Systems, Vol. 102, pp. 426-436, Jan. 2020, https://doi.org/10.1016/j.future.2019.08.031 | es_ES |
dc.description.abstract | [Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have focused on developing new methods that increase the global relevance of the subset of selected features while reducing the redundancy of information. However, those methods that select features with high relevance and low redundancy are extremely time-consuming when processing large datasets. In this work we present CUDA-JMI, a tool based on Joint Mutual Information that accelerates feature selection by exploiting the computational capabilities of modern heterogeneous systems that contain several CPU cores and GPU devices. The experimental evaluation has been carried out in three systems with different type and amount of CPUs and GPUs using five publicly available datasets from different fields. These results show that CUDA-JMI is significantly faster than its original sequential counterpart for all systems and input datasets. For instance, the runtime of CUDA-JMI is up to 52 times faster than an existing sequential JMI-based implementation in a machine with 24 CPU cores and two NVIDIA M60 boards (four GPUs). CUDA-JMI is publicly available to download from https://sourceforge.net/projects/cuda-jmi | es_ES |
dc.description.sponsorship | This research has been partially funded by projects TIN2016-75845-P and TIN-2015-65069-C2-1-R of the Ministry of Economy, Industry and Competitiveness of Spain, as well as by Xunta de Galicia, Spain projects ED431D R2016/045, ED431G/01 and GRC2014/035, all of them partially funded by FEDER, Spain funds of the European Union. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D R2016/045 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-75845-P/ES/NUEVOS DESAFIOS EN COMPUTACION DE ALTAS PRESTACIONES: DESDE ARQUITECTURAS HASTA APLICACIONES (II)/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-65069-C2-1-R/ES/ALGORITMOS ESCALABLES DE APRENDIZAJE COMPUTACIONAL: MAS ALLA DE LA CLASIFICACION Y LA REGRESION | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.future.2019.08.031 | |
dc.relation.uri | https://doi.org/10.1016/j.future.2019.08.031 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Feature selection | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | CUDA | es_ES |
dc.subject | GPU | es_ES |
dc.subject | Multithreading | es_ES |
dc.title | CUDA-JMI: Acceleration of feature selection on heterogeneous systems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Future Generation Computer Systems | es_ES |
UDC.volume | 102 | es_ES |
UDC.startPage | 426 | es_ES |
UDC.endPage | 436 | es_ES |
dc.identifier.doi | 10.1016/j.future.2019.08.031 |
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