Efficient algorithms for parallelizing Monte Carlo simulations for 2D Ising spin models

Eunice E. Santos, Jeffrey M. Rickman, Gayathri Muthukrishnan, Shuangtong Feng

Research output: Contribution to journalArticle

Abstract

In this paper, we design and implement a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyze our parallel algorithms on LogP, a portable and general parallel machine model. We then obtain rigorous theoretical runtime results on LogP for all the parallel algorithms. Moreover, a guiding equation is derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regard to random spin selection, we are able to develop parallel algorithms with efficient communication schemes. We introduce two novel schemes, namely the FML scheme and the α-scheme. We analyze randomness of our schemes using statistical methods and provide comparisons between the different schemes.

Original languageEnglish (US)
Pages (from-to)274-290
Number of pages17
JournalJournal of Supercomputing
Volume44
Issue number3
DOIs
StatePublished - Jun 1 2008
Externally publishedYes

Keywords

  • Algorithm design and analysis
  • Computational science
  • Data layout optimization
  • Ising model
  • LogP model
  • Parallel computing
  • Parallel models
  • Performance prediction

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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