一种三维AMR数据集的并行分割算法

Andreas Bleuler, Romain Teyssier, Sébastien Carassou, Davide Martizzi
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引用次数: 24

摘要

本文介绍了一种新的天体物理流体模拟结构检测分割算法——并行分层分水岭(Parallel HiErarchical Watershed, phew),并将其实现到自适应网格细化(AMR)代码中。Phew在自适应网格上定义的密度场上工作,因此可以在粒子投影到网格上后用于气体密度或暗物质密度。该算法基于对计算量的“分水岭”分割成密集区域,然后根据密度场的鞍点拓扑合并分割的补丁。Phew能够自动检测超过所采用密度阈值的连接区域,以及其中的整个子结构集。我们的算法是完全并行的,并使用MPI库。我们详细地描述了并行算法,并进行了一个扩展实验,证明了phew在大规模并行系统上有效运行的能力。未来的工作将在我们的分割算法中增加一个粒子解绑定过程和晕属性的计算,从而将phew的范围扩展到真正的晕发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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PHEW: a parallel segmentation algorithm for three-dimensional AMR datasets

We introduce phew (Parallel HiErarchical Watershed), a new segmentation algorithm to detect structures in astrophysical fluid simulations, and its implementation into the adaptive mesh refinement (AMR) code ramses. phew works on the density field defined on the adaptive mesh, and can thus be used on the gas density or the dark matter density after a projection of the particles onto the grid. The algorithm is based on a ‘watershed’ segmentation of the computational volume into dense regions, followed by a merging of the segmented patches based on the saddle point topology of the density field. phew is capable of automatically detecting connected regions above the adopted density threshold, as well as the entire set of substructures within. Our algorithm is fully parallel and uses the MPI library. We describe in great detail the parallel algorithm and perform a scaling experiment which proves the capability of phew to run efficiently on massively parallel systems. Future work will add a particle unbinding procedure and the calculation of halo properties onto our segmentation algorithm, thus expanding the scope of phew to genuine halo finding.

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期刊介绍: Computational Astrophysics and Cosmology (CompAC) is now closed and no longer accepting submissions. However, we would like to assure you that Springer will maintain an archive of all articles published in CompAC, ensuring their accessibility through SpringerLink's comprehensive search functionality.
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