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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/83862

    Título
    UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications
    Autor
    Carratalá-Sáez, Rocío
    Torres de la Sierra, YuriAutoridad UVA Orcid
    Sierra Pallarés, José BenitoAutoridad UVA Orcid
    López Huguet, Sergio
    Llanos Ferraris, Diego RafaelAutoridad UVA Orcid
    Año del Documento
    2023
    Editorial
    Springer Nature
    Descripción
    Producción Científica
    Documento Fuente
    UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications, Carratalá-Sáez, R., Torres, Y., Sierra-Pallares, J. et al.. Journal of Supercomputing 79, 9635–9665 (2023).
    Abstract
    The determination of Lagrangian Coherent Structures (LCS) is becoming very important in several disciplines, including cardiovascular engineering, aerodynamics, and geophysical fluid dynamics. From the computational point of view, the extraction of LCS consists of two main steps: The flowmap computation and the resolution of Finite Time Lyapunov Exponents (FTLE). In this work, we focus on the design, implementation, and parallelization of the FTLE resolution. We offer an in-depth analysis of this procedure, as well as an open source C implementation (UVaFTLE) parallelized using OpenMP directives to attain a fair parallel efficiency in shared-memory environments. We have also implemented CUDA kernels that allow UVaFTLE to leverage as many NVIDIA GPU devices as desired in order to reach the best parallel efficiency. For the sake of reproducibility and in order to contribute to open science, our code is publicly available through GitHub. Moreover, we also provide Docker containers to ease its usage.
    Materias (normalizadas)
    Informática
    Materias Unesco
    1203 Ciencia de Los Ordenadores
    3304 Tecnología de Los Ordenadores
    Palabras Clave
    Finite time Lyapunov exponent, Lagrangian coherent structures, OpenMP, GPU, Multithreading, Multi-GPU
    ISSN
    0920-8542
    Revisión por pares
    SI
    DOI
    10.1007/s11227-022-05017-x
    Patrocinador
    This work has been funded by the Consejería de Educación of Junta de Castilla y León, Ministerio de Economía, Industria y Competitividad of Spain, European Regional Development Fund (ERDF) program: Project PCAS (TIN2017-88614-R) and Project PROPHET-2 (VA226P20). This work was supported in part by grant TED2021-130367B-I00 funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”. Jose Sierra-Pallares was supported by project VA182P20 from Junta de Castilla y León. The experiments carried out using the CESGA resources were possible thanks to the Red Española de Supercomputación (RES) projects IM-2022-2-0015 and IM-2022-3-0021.
    Version del Editor
    https://link.springer.com/article/10.1007/s11227-022-05017-x
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/83862
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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    • DEP41 - Artículos de revista [147]
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    Universidad de Valladolid

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