Diagnosability of component-Composition graphs in the mm* model

Chia Wei Lee, Sun-Yuan Hsieh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Diagnosability is an important metric for measuring the reliability of multiprocessor systems. This article adopts the MM* model and outlines the common properties of a wide class of interconnection networks, called component-composition graphs (CCGs), to determine their diagnosability by using their obtained properties. By applying the results to multiprocessor systems, the diagnosability of hypercube-like networks (including hypercubes, crossed cubes, Möbius cubes, twisted cubes, locally twisted cubes, generalized twisted cubes,and recursive circulants), star graphs, pancake graphs, bubble-sort graphs, and burnt pancake graphs, all of which belong to the class of CCGs, can also be computed.

Original languageEnglish
Article number27
JournalACM Transactions on Design Automation of Electronic Systems
Volume19
Issue number3
DOIs
Publication statusPublished - 2014 Jan 1

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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