Coarse grained parallel next element search

Albert Chan, F. Dehne, A. Rau-Chaplin
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引用次数: 9

Abstract

The authors present a parallel algorithm for solving the next element search problem on a set of line segments, using a BSP like model referred to as the coarse grained multicomputer (CGM). The algorithm requires O(1) communication rounds (h-relations with h=O(n/p)), O((n/p) log n) local computation, and O((n/p) log n) storage per processor. The result implies solutions to the point location, trapezoidal decomposition and polygon triangulation problems. A simplified version for axis parallel segments requires only O(n/p) storage per processor, and they discuss an implementation of this version. As in a previous paper by Develliers and Fabri (1993), their algorithm is based on a distributed implementation of segment trees which are of size O(n log n). The paper improves on the work of Develliers and Fabri which presented a CGM algorithm for the special case of trapezoidal decomposition only and requires O((n/p)*log p*log n) local computation.
粗粒度并行下一个元素搜索
作者提出了一种求解线段集上的下一元素搜索问题的并行算法,该算法使用一种类似BSP的模型,称为粗粒度多计算机(CGM)。该算法需要O(1)轮通信(h-关系,h=O(n/p)), O((n/p) log n)个局部计算,以及O((n/p) log n)个处理器存储。该结果为点定位、梯形分解和多边形三角剖分问题提供了解决方案。轴平行段的简化版本只需要每个处理器O(n/p)存储空间,他们讨论了这个版本的实现。与develers和Fabri(1993)之前的论文一样,他们的算法基于大小为O(n log n)的段树的分布式实现。本文改进了develers和Fabri的工作,该工作仅针对梯形分解的特殊情况提出了CGM算法,并且需要O((n/p)*log p*log n)局部计算。
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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