A scalable low discrepancy point generator for parallel computing

Kwong Ip LIU*, Fred J. Hickernell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The Monte Carlo (MC) method is a simple but effective way to perform simulations involving complicated or multivariate functions. The Quasi-Monte Carlo (QMC) method is similar but replaces independent and identically distributed (i.i.d.) random points by low discrepancy points. Low discrepancy points are regularly distributed points that may be deterministic or randomized. The digital net is a kind of low discrepancy point set that is generated by number theoretical methods. A software library for low discrepancy point generation has been developed. It is thread-safe and supports MPI for parallel computation. A numerical example from physics is shown.

Original languageEnglish
Pages (from-to)257-262
Number of pages6
JournalLecture Notes in Computer Science
Publication statusPublished - 2004

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Digital nets
  • Monte Carlo and Quasi-Monte Carlo methods
  • Parallel programming
  • Software library


Dive into the research topics of 'A scalable low discrepancy point generator for parallel computing'. Together they form a unique fingerprint.

Cite this