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dc.contributor.authorCano, Alberto
dc.contributor.authorVentura Soto, S.
dc.contributor.authorCios, Krzysztof J.
dc.date.accessioned2017-01-19T13:15:02Z
dc.date.available2017-01-19T13:15:02Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10396/14346
dc.description.abstractCAIM(Class-Attribute InterdependenceMaximization) is one of the stateof- the-art algorithms for discretizing data for which classes are known. However, it may take a long time when run on high-dimensional large-scale data, with large number of attributes and/or instances. This paper presents a solution to this problem by introducing a GPU-based implementation of the CAIM algorithm that significantly speeds up the discretization process on big complex data sets. The GPU-based implementation is scalable to multiple GPU devices and enables the use of concurrent kernels execution capabilities ofmodernGPUs. The CAIMGPU-basedmodel is evaluated and compared with the original CAIM using single and multi-threaded parallel configurations on 40 data sets with different characteristics. The results show great speedup, up to 139 times faster using 4 GPUs, which makes discretization of big data efficient and manageable. For example, discretization time of one big data set is reduced from 2 hours to less than 2 minuteses_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.subjectSupervised discretizationes_ES
dc.subjectParallel implementation of CAIM algorithmes_ES
dc.subjectGPUes_ES
dc.subjectCUDAes_ES
dc.titleScalable CAIM Discretization on Multiple GPUs Using Concurrent Kernelses_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.relation.projectIDGobierno de España. TIN-2011-22408 (SV)es_ES
dc.relation.projectIDGobierno de España. FPU AP2010-0042 (AC)es_ES
dc.relation.projectID1R01HD056235-01A1 (KJC)es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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