2011
DOI: 10.1016/j.amc.2011.05.041
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An adaptive domain-decomposition technique for parallelization of the fast marching method

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Cited by 20 publications
(25 citation statements)
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References 33 publications
(35 reference statements)
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“…The parallel re-initialization algorithm is ap- To avoid the approximation error introduced by the usage of k-d trees, we chose m ¼ jF [ Lj as previously used for the error analysis in Section 7. In Table 2, the error and the rate of convergence of the re-initialization algorithm are shown if disturbing u sp by (17). The rate of convergence (ROC) is given by the quotient of the error on two consecutive triangulations.…”
Section: Numerical Resultsmentioning
confidence: 99%
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“…The parallel re-initialization algorithm is ap- To avoid the approximation error introduced by the usage of k-d trees, we chose m ¼ jF [ Lj as previously used for the error analysis in Section 7. In Table 2, the error and the rate of convergence of the re-initialization algorithm are shown if disturbing u sp by (17). The rate of convergence (ROC) is given by the quotient of the error on two consecutive triangulations.…”
Section: Numerical Resultsmentioning
confidence: 99%
“…This process is iteratively refined exchanging data across sub-domain boundaries. In contrast to a domain decomposition approach, the parallel FMM presented in [17] is based on distributing the interface and is discussed for Cartesian grids. To the best of our knowledge, there is no publication of any parallel FMM on unstructured grids.…”
Section: Algorithms For Solving the Eikonal Equationmentioning
confidence: 99%
“…An other method is proposed in [16] where the decomposition is made on the narrow-band itself. The initial front is partitioned at the initialization and then each processor solves a sub-problem independently ( fig.…”
Section: B Narrow Band Partitioning For the Fmmmentioning
confidence: 99%
“…Fair load balancing is a real concern in [16] where the authors proposed to choose the sets in such a way that the emerging wave fronts ideally cover nearly the same portions of the computational domain. However, this assumes that we know the behavior of the solution.…”
Section: Fine-grained Parallel Model For the Fimmentioning
confidence: 99%
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