Efficient implementation of multilevel algorithms on hypercube supercomputers for computer vision

Sotirios G. Ziavras, Devenkumar P. Shah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The authors deal with the problem of implementing multilevel algorithms on hypercube parallel computers for computer vision problems. The conventional pyramid is a multilevel structure with identical 2 × 2 reductions between all pairs of neighboring levels. The mapping algorithms proposed for source multilevel structures and target parallel computers implementing the hypercube topology are based on some of the most important pyramid mapping algorithms. Application algorithms are classified according to their performance requirements and the most appropriate mapping algorithms are then chosen for their implementation The new mapping algorithms are compared according to a set of graph embedding metrics and from results produced on a Connection Machine system CM-2 massively parallel computer. The comparison of the CM-2 results shows that the selection of multilevel structures other than the pyramid for the implementation of algorithms is a more efficient solution most of the time. In addition, the selection of the most appropriate mapping algorithm often becomes a very critical decision for high yields.

Original languageEnglish (US)
Title of host publicationProceedings - 1993 Computer Architectures for Machine Perception, CAMP 1993
EditorsLarry S. Davis, Magdy A. Bayoumi, Kimon P. Valavanis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages313-322
Number of pages10
ISBN (Electronic)0818654201, 9780818654206
DOIs
StatePublished - 1993
Event1993 Computer Architectures for Machine Perception, CAMP 1993 - New Orleans, United States
Duration: Dec 15 1993Dec 17 1993

Publication series

NameProceedings - 1993 Computer Architectures for Machine Perception, CAMP 1993

Conference

Conference1993 Computer Architectures for Machine Perception, CAMP 1993
CountryUnited States
CityNew Orleans
Period12/15/9312/17/93

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Signal Processing
  • Computer Vision and Pattern Recognition

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