An Empirical Study of Combining Communicating Processes in a Parallel Discrete Event Simulation

Abstract

The primary goal of distributed discrete event simulations is to achieve speedup in simulation execution time by distributing the processing of the simulation over multiple processors. When partitioned for distribution in this fashion, simulations are typically partitioned such that there are more processes than processors. This thesis reviews existing methods for distributed discrete event simulations, and proposes general guidelines for efficient partitioning for a given communications topology based on empirical evidence. A performance analysis is conducted for two approaches to partitioning the system. The first method chosen is a mapping of multiple processes to a processor and the second approach utilizes a distributed event list approach, developed by Mannix. This approach combines smaller processes into a larger single process, incorporating a next event list similar to that used in a sequential simulation. Empirical studies compare the performance of the two approaches under a variety of conditions. The traditional Chandy-Mirsa approach to system partitioning is demonstrated to yield overall better performance than the distributed event list algorithm. General guidelines for partitioning the system for both approaches are developed based on the performance comparisons.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA230982

Entities

People

  • Ann K. Lee

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Application Software
  • Combat Simulations
  • Computations
  • Computer Programming
  • Computer Simulations
  • Computers
  • Computing System Architectures
  • Detection
  • Mathematics
  • Simulations
  • Simulators
  • Topology
  • Very Large Scale Integration
  • Workload

Fields of Study

  • Computer science
  • Engineering

Readers

  • Parallel and Distributed Computing.
  • Systems Analysis and Design