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Comparison of the Parallel Fast Marching Method, the Fast Iterative Method, and the Parallel Semi-ordered Fast Iterative Method

机译:并行快速行进方法,快速迭代方法和并行半阶快速迭代方法的比较

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Solving the eikonal equation allows to compute a monotone front propagation of anisotropic nature and is thus a widely applied technique in different areas of science and engineering. Various methods are available out of which only a subset is suitable for shared-memory parallelization, which is the key focus of this analysis. We evaluate three different approaches, those being the recently developed parallel fast marching method based on domain decompositioning, the inherently parallel fast iterative method, and a parallel approach of the semi-ordered fast iterative method, which offers increased stability for variations in the front velocity as compared to established iterative methods. We introduce the individual algorithms, evaluate the accuracy, and show benchmark results based on a dual socket Intel Ivy Bridge-EP cluster node using C++/OpenMP implementations. Our investigations show that the parallel fast marching method performs best in terms of accuracy and single thread performance and reasonably well with respect to parallel efficiency for up to 8-16 threads.
机译:求解该方程式方程可以计算出各向异性性质的单调前沿传播,因此是科学和工程学不同领域中广泛应用的技术。可以使用多种方法,其中只有一个子集适用于共享内存并行化,这是此分析的重点。我们评估了三种不同的方法,它们是最近开发的基于域分解的并行快速行进方法,固有的并行快速迭代方法以及半阶快速迭代方法的并行方法,这为前速度的变化提供了更高的稳定性与已建立的迭代方法相比。我们介绍了各个算法,评估了准确性,并基于双插槽Intel Ivy Bridge-EP群集节点(使用C ++ / OpenMP实现)显示了基准测试结果。我们的研究表明,并行快速行进方法在准确性和单线程性能方面表现最佳,并且在多达8-16个线程的并行效率方面表现也相当不错。

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