Exploiting multi-core architectures in clusters for enhancing the performance of the parallel Bootstrap simulation algorithm

César A.F. De Rose*, Paulo Fernandes, Antonio M. Lima, Afonso Sales, Thais Webber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The solution of Markovian models is usually nontrivial to be performed using iterative methods, so it is wellfitted to simulation approaches and high performance implementations. The Bootstrap simulation method is a novel simulation technique of Markovian models that brings a considerable improvement in the results accuracy, notwithstanding its higher computation cost when compared to other simulation alternatives. In this paper, we present three parallel implementations of the Bootstrap simulation algorithm, exploiting a multi-core SMP cluster. We discuss some practical implementation issues about processing and communication demands, as well as present an analysis of speedup and efficiency considering different models' sizes and simulation trajectory lengths. Finally, future works point out some improvements to achieve even better results in terms of accuracy.

Original languageEnglish
Title of host publication2011 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2011
Pages1442-1451
Number of pages10
DOIs
Publication statusPublished - 2011
Event25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011 - Anchorage, AK, United States
Duration: 16 May 201120 May 2011

Publication series

NameIEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum

Conference

Conference25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011
Country/TerritoryUnited States
CityAnchorage, AK
Period16/05/1120/05/11

Keywords

  • Discrete-event simulation
  • Markovian models
  • Multi-core SMP cluster
  • Parallel algorithms
  • Performance evaluation
  • Statistical techniques

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