Software Performance Modeling in PC Clusters

Abstract

Execution of course grain parallel programs in PC clusters promises super-computer performance in low cost hardware environments. However the overhead associated with data distribution, synchronization, and peripheral access can easily eliminate any performance gain promised by the individual cluster capacity. Application specific system performance analysis is required both to engineer PC cluster hardware and evaluate the cost effectiveness of parallelizing software components. This paper presents a distributed system performance model and software analysis methodology suitable for estimating the execution times of large grain parallel application programs in clusters of PC hardware. The performance model emphasizes the use of application hardware performance results readily available in most systems. These are combined with single thread application software resource requirements in order to estimate the achievable execution rates in target clusters. A case study of the analysis of a video realistic battlefield simulator implementation in a PC cluster running under Linux is presented. Benchmark results and performance estimates for specific candidate hardware configurations are calculated and compared with actual results.

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Document Details

Document Type
Technical Report
Publication Date
Jul 24, 2009
Accession Number
ADA502987

Entities

People

  • Steve Decato
  • Wolfgang Baer

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Software
  • Central Processing Units
  • Communication Channels
  • Communication Systems
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Data Transmission
  • Digital Communications
  • High Resolution
  • Network Protocols
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Software Development

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.