Abstract
A parallel algorithm for simultaneous untangling and smoothing of tetrahedral meshes is proposed in this paper. This algorithm is derived from a sequential mesh optimization method. We provide a detailed analysis of its parallel scalability and efficiency, load balancing, and parallelism bottlenecks using six benchmark meshes. In addition, the influence of three previously-published graph coloring techniques on the performance of our parallel algorithm is evaluated. We demonstrate that the proposed algorithm is highly scalable when run on a shared-memory computer with up to 128 Itanium 2 processors. However, some parallel deterioration is observed. Here, we analyze its main causes using a theoretical performance model and experimental results. © 2014 Springer-Verlag.
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Benitez, D., Rodríguez, E., Escobar, J. M., & Montenegro, R. (2014). The effect of parallelization on a tetrahedral mesh optimization method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8385 LNCS, pp. 163–173). Springer Verlag. https://doi.org/10.1007/978-3-642-55195-6_15
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