Parallel Algorithms with Processor Failures and Delays

Abstract

We study efficient deterministic parallel algorithms on two models: restartable fail-stop CRCW PRAMs and strongly asynchronous PRAMs. In the first model, synchronous processors are subject to arbitrary stop failures and restarts determined by an on-line adversary and involving loss of private but not shared memory; the complexity measures are completed work (where processors are charged for completed fixed-size update cycles) and overhead ratio (completed work amortized over necessary work and failure). In the second model, the result of the computation is a serialization of the actions of the processors determined by an on-line adversary; the complexity measure is total work (number of steps taken by all processors). Despite their differences the two models share key algorithmic techniques. We present new algorithms for the Write-All problem (in which P processors write ones into an array of size N) for these two models. These algorithms can be used to implement a simulation strategy for any N processor PRAM on a restartable fail-stop P processor CRCW PRAM such that it guarantees a terminating execution of each simulated N processor step, with O(log sq N) overhead ratio.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1991
Accession Number
ADA242764

Entities

People

  • Alex A. Shvartsman
  • Jonathan F. Buss
  • Paris C. Kanellakis
  • Prabhakar L. Radge

Organizations

  • Brown University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Collisions
  • Computations
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Distributed Computing
  • Fault Tolerance
  • Instruction Set Architecture
  • Network Architecture
  • Operating Systems
  • Parallel Computing
  • Simulations
  • Standards
  • Theoretical Computer Science
  • Trees (Data Structures)

Fields of Study

  • Computer science
  • Engineering

Readers

  • Graph Algorithms and Convex Optimization.
  • Parallel and Distributed Computing.