A comparison of vectorizable discrete sampling methods in Monte Carlo applications

Riyanarto Sarno*, Virendra C. Bhavsar, Esam M.A. Hussein

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


The performance of various vectorizable discrete random-sampling methods, along with the commonly used inverse sampling method, is assessed on a vector machine. Monte Carlo applications involving, one-dimensional, two-dimensional and multi-dimensional probability tables are used in the investigation. Various forms of the weighted sampling method and methods that transform the original probability table are examined. It is found that some form of weighted sampling is efficient, when the original probability distribution is not far from uniform or can be approximated analytically. Table transformation methods, though requiring additional memory storage, are best suited in applications where multidimensional tables are involved.

Original languageEnglish
Pages (from-to)295-305
Number of pages11
JournalInternational Journal of High Speed Computing
Issue number3
Publication statusPublished - Sept 1996
Externally publishedYes


  • Discrete sampling
  • Monte Carlo simulations
  • Vector processing
  • Weighted sampling


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