Distributed Computing for Signal Processing: Topological Properties of Interconnection Networks for Parallel Processors. Appendix E. A Unified Approach.

Abstract

Two methods are used to speed up the execution of a computational task. One is new technology development and the other is the exploitation of parallelism in the computation. To take an advantage of the parallelism in a task requires the utilization of parallel computer architectures. At a certain high level of abstraction a parallel computer system is represented as a graph where the nodes represent processors, memories, or other devices, and the edges represent the communication links. In this thesis the following problems of parallel processing are studied. First is a theoretical study of topological properties of interconnection networks. Second is a case study of a network design for a real-time system. Lastly, the use of SIMD(Single Instruction Stream Multiple Data Stream) networks for performing 'shuffles'. A general model that can be used to describe networks and systems with arbitrary topologies is developed. Based upon the of morphism of groups, the concept of morphism of systems is developed. The morphism of systems is called quasimorphism and allows a method of comparison between topologically arbitrary parallel computer systems. The quasimorphism is used to study the emulation of one system by another.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1985
Accession Number
ADA167336

Entities

People

  • Robert R. Seban

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Case Studies
  • Computers
  • Data Transmission
  • Detection
  • Digital Communications
  • Distributed Computing
  • Electrical Engineering
  • Engineering
  • Error Correction Codes
  • Graph Theory
  • Image Processing
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Pattern Recognition
  • Signal Processing
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Computer Engineering
  • Computer Networking
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML