Simulation of Fault Tolerance in a Hypercube Arrangement of Discrete Processors.

Abstract

The purpose of this study was to implement a technique for fault-tolerant parallel computation on the Intel Corporation's Hypercube computer. This work was motivated by the recent progress in parallel computation and neural network techniques. This study focuses on the implementation of one particular type of parallel processing architecture on the Intel Hypercube. The architecture in question is known as the cube-connected cycle (CCC). This architecture is used as a basis for a reconfiguration scheme known as reconfigurable cube-connected cycles. The aim of this reconfiguration is to build a parallel computing system with fault tolerance capability. Implementation of this technique on the Intel Hypercube was by simulation. The loading of only part of the hypercube available nodes, holding the remaining nodes in reserve was accomplished, followed by a simulation of the replacement of a deactivated node with a spare node. Conclusions are reached regarding the suitability of the Intel machine for fault tolerance experiments versus the rapid computation for which it was designed. Recommendations are made regarding the next logical steps in continuation of the work presented in this study.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA189682

Entities

People

  • Gil Zilberstein

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Computer Architecture
  • Computer Programming
  • Computer Programs
  • Computers
  • Computing Devices
  • Fault Tolerance
  • National Security
  • Neural Networks
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Pattern Recognition
  • Simulations
  • Space Systems
  • Spacecraft

Readers

  • Computer Networking
  • Computer Science.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference