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Convexity algorithms for parallel machines.

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The authors present a parallel algorithms to identify (i.e., detect and enumerate) the extreme points of the convex hull of a set of planar points using a hypercube, pyramid, tree, mesh-of-trees, mesh with reconfigurable bus, EREW PRAM (exclusive read, exclusive write parallel random-access machine) and AKS sorting network. For the situation where the input set of n planar points is given ordered one per processor on a machine with Θ(n) processors, the authors introduce a worst-case hypercube algorithm that finishes in Θ(log n) time, a worst-case algorithm for the pyramid, tree, and mesh-of-trees that finishes in Θ(log3 n/log log n)-time, and a worst-case algorithm for the mesh with reconfigurable bus that finishes in Θ(log2 n) time. For the situation where the input set is given unordered one per processor, several optimal worst-case algorithms are given. It is shown that the Θ(log n) time hypercube algorithm algorithm for ordered data will extend to yield a worst-case optimal EREW PRAM algorithm and AKS sorting network algorithm for the case where the set of planar points is distributed in a random fashion one point per processor.

Original languageEnglish
Title of host publicationProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit
PublisherPubl by IEEE
Pages918-924
Number of pages7
ISBN (Print)0818608625
StatePublished - 1988

Publication series

NameProc CVPR 88 Comput Soc Conf on Comput Vision and Pattern Recognit

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