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dc.contributor.advisorAsenjo-Plaza, Rafael 
dc.contributor.advisorRodríguez-Moreno, Andrés 
dc.contributor.authorRomero Moreno, José Carlos
dc.contributor.otherArquitectura de Computadoreses_ES
dc.date.accessioned2023-01-30T13:15:31Z
dc.date.available2023-01-30T13:15:31Z
dc.date.issued2023-01
dc.date.submitted2022-09-15
dc.identifier.urihttps://hdl.handle.net/10630/25823
dc.descriptionOur experimental results show that, our heterogeneous CPU+GPU approaches always outperform only-CPU and only-GPU state-of-the-art implementations up to 6.86x and 5.19x, respectively, and they fall below 6% of ideal peak performance.es_ES
dc.description.abstractIn the last few years, the heterogeneous architectures have become dominant in each part of the computing industry: from heterogeneous GPU accelerators joining multi-core CPUs within the same chip, to Systems on Chip that integrate DSPs or. The main motivation of this thesis is the fact that there is no implementation with optimal solution for heterogeneous architectures for two massive data, real-life and complex problems widely used in big data fields: Time Series and the Skyline problem. Firstly, we focus on the motifs/discord discovery problem for Time Series, taking as a starting point the state-of-the-art algorithm, the Matrix Profile. We present the first heterogeneous implementations for the Matrix Profile computation for CPU + GPU architectures and CPU + FPGA using a High Performance FPGA with integrated High Bandwidth Memory, HBM. We propose Fastfit, a hierarchical scheduler that efficiently balances workload among the FPGA and the CPU cores and computes an even partition so that all FPGA IPs complete their assignment at the same time. We validate the accuracy of our models, finding that it outperforms state-of-the-art previous schedulers by achieving up to 99.4% of ideal performance. Secondly, we tackle the problem of computing the Skyline operator over a stream of independent data queries targeting a heterogeneous CPU + GPU architecture. We contribute with a novel heterogeneous implementation, based on oneAPI, of the state-of-the-art SkyAlign algorithm. We design a graph-based engine, SkyFlow, and propose two heterogeneous approaches for Skyline computation over a stream of data queries: the first keeps two Skyline computations in parallel, one per device, and the second splits a single Skyline computation between the CPU and GPU.es_ES
dc.language.isoenges_ES
dc.publisherUMA Editoriales_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArquitectura de ordenadores - Tesis doctoraleses_ES
dc.subject.otherSkylinees_ES
dc.subject.otherTime Serieses_ES
dc.subject.otherHigh Performance FPGAes_ES
dc.subject.otherHeterougeneous computinges_ES
dc.subject.otherOneAPIes_ES
dc.titleOptimization of massive data applications on heterogeneous architectureses_ES
dc.typeinfo:eu-repo/semantics/doctoralThesises_ES
dc.centroE.T.S. de Arquitecturaes_ES
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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