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• W2288712670 abstract "The characterization of nonlinear dynamical systems and their attractors in terms of invariant measures, basins of attractions and the structure of their vector fields usually outlines a task strongly related to the underlying computational cost. In this work, the practical aspects related to the use of parallel computing – specially the use of Graphics Processing Units (GPUs) and of the Compute Unified Device Architecture (CUDA) – are reviewed and discussed in the context of nonlinear dynamical systems characterization. In this work such characterization is performed by obtaining both local and global Lyapunov exponents for the classical forced Duffing oscillator. The local divergence measure was employed by the computation of the Lagrangian Coherent Structures (LCSs), revealing the general organization of the flow according to the obtained separatrices, while the global Lyapunov exponents were used to characterize the attractors obtained under one or more bifurcation parameters. These simulation sets also illustrate the required computation time and speedup gains provided by different parallel computing strategies, justifying the employment and the relevance of GPUs and CUDA in such extensive numerical approach. Finally, more than simply providing an overview supported by a representative set of simulations, this work also aims to be a unified introduction to the use of the mentioned parallel computing tools in the context of nonlinear dynamical systems, providing codes and examples to be executed in MATLAB and using the CUDA environment, something that is usually fragmented in different scientific communities and restricted to specialists on parallel computing strategies." @default.
• W2288712670 created "2016-06-24" @default.
• W2288712670 creator A5030047879 @default.
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• W2288712670 date "2016-08-01" @default.
• W2288712670 modified "2023-10-12" @default.
• W2288712670 title "Numerical characterization of nonlinear dynamical systems using parallel computing: The role of GPUs approach" @default.
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• W2288712670 doi "https://doi.org/10.1016/j.cnsns.2015.12.021" @default.
• W2288712670 hasPublicationYear "2016" @default.
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