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Parallel physical optimization algorithms for data mapping

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 634)

Abstract

Parallel algorithms, based on simulated annealing, neural networks and genetic algorithms, for mapping irregular data to multicomputers are presented and compared. The three algorithms deviate from the sequential versions in order to achieve acceptable speed-ups. The parallel annealing and neural algorithms include communication schemes adapted to the properties of the mapping problem and of the algorithms themselves. These schemes arc found useful for providing both good solutions and reasonable execution times. The parallel genetic algorithm is based on a model of natural evolution. The three algorithms preserve the high quality solutions and the non-bias properties of their sequential counterparts. Further, the comparison results show their suitability for different requirements of mapping time and quality.

Keywords

High Quality Solution Parallel Annealing Parallel Execution Time Sequential Genetic Algorithm Node Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  1. 1.Syracuse Ctr. for Computational Sc.USA
  2. 2.Beirut University CollegeLebanon
  3. 3.Northeast Parallel Architectures Ctr.Syracuse UniversitySyracuseUSA

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