Optimal implementation of general divide-and-conquer on the hypercube and related networks

Ernst W. Mayr, Ralph Werchner

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

Abstract

We show how to implement divide-and-conquer algorithms without undue overhead on a wide class of networks. We give an optimal generic divide-and-conquer implementation on hypercubes for the class of divide-andconquer algorithms for which the total size of the subproblems on any level of recursion does not exceed the original problem size. For this implementation, appropriately sized subcubes have to be allocated to the subproblems generated by the divide-step. We take care that these allocation steps do not cause unbalanced distribution of work, and that, asymptotically, they do not increase the running time. Variants of our generic algorithm also work for the butterfly network and, by a general simulation, for the class of hypercubic networks, including the shuflte-exchange and the cube-connected-cycles network. Our results can also be applied to optimally solve various types of routing problems.

Original languageEnglish
Title of host publicationParallel Architectures and Their Efficient Use - First Heinz Nixdorf Symposium, Proceedings
EditorsFriedhelm Meyer auf der Heide, Burkhard Monien, Arnold L. Rosenberg
PublisherSpringer Verlag
Pages195-206
Number of pages12
ISBN (Print)9783540567318
DOIs
StatePublished - 1993
Externally publishedYes
Event1st Heinz Nixdorf Symposium on Parallel Architectures and Their Efficient Use, 1992 - Paderborn, Germany
Duration: 11 Nov 199213 Nov 1992

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Heinz Nixdorf Symposium on Parallel Architectures and Their Efficient Use, 1992
Country/TerritoryGermany
CityPaderborn
Period11/11/9213/11/92

Keywords

  • Algorithms and Data Structures
  • Theory of Parallel and Distributed Computation

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