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A Parallel Symbolic Computation Environment: Structures and Mechanics

  • ’Mantŝika Matooane
  • Arthur Norman
Conference paper
  • 76 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1685)

Abstract

We describe a set of representations for polynomials and sparse matrices suited for use with fine-grain parallelism on a distributed memory multiprocessor system. Our aim is to support use of supercomputers with this style of architecture to perform computations that would exceed the main memory capacity of more traditional computers: although such systems have very high performance communication networks it is still essential to avoid letting any one part of the network become a bottleneck. We use randomised data placement both to avoid hot-spots in the communication patterns and to balance (in a probabilistic sense) the memory load placed upon each processing element. The expected application areas for such a system will be those where intermediate expression swell means that the huge primary memory available on MPP systems will be needed if the smaller final result is to be successfully computed.

Keywords

Processing Element Memory Load Hash Table Sparse Matrice Distribute Memory Architecture 
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 1999

Authors and Affiliations

  • ’Mantŝika Matooane
    • 1
  • Arthur Norman
    • 1
  1. 1.University of Cambridge Computer LaboratoryCambridgeUK

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