Using RngStreams for parallel random number generation in C++ and R

Andrew T. Karl; Randy Eubank; Jelena Milovanovic; Mark Reiser; Dennis Young

doi:10.1007/s00180-014-0492-3

Using RngStreams for parallel random number generation in C++ and R

Andrew T. Karl, Randy Eubank, Jelena Milovanovic, Mark Reiser, Dennis Young

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using RngStreams with C++ programs that are parallelized via OpenMP or MPI. Ways to access the backbone generator from RngStreams in R through the parallel and rstream packages are also described. The ideas in the paper are illustrated with both a simple running example and a Monte Carlo integration application.

Original language	English (US)
Pages (from-to)	1301-1320
Number of pages	20
Journal	Computational Statistics
Volume	29
Issue number	5
DOIs	https://doi.org/10.1007/s00180-014-0492-3
State	Published - Oct 2014

Keywords

MPI
Multicore
OpenMP
Rstream

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Computational Mathematics

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10.1007/s00180-014-0492-3

Cite this

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abstract = "The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using RngStreams with C++ programs that are parallelized via OpenMP or MPI. Ways to access the backbone generator from RngStreams in R through the parallel and rstream packages are also described. The ideas in the paper are illustrated with both a simple running example and a Monte Carlo integration application.",

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AU - Reiser, Mark

AU - Young, Dennis

PY - 2014/10

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AB - The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using RngStreams with C++ programs that are parallelized via OpenMP or MPI. Ways to access the backbone generator from RngStreams in R through the parallel and rstream packages are also described. The ideas in the paper are illustrated with both a simple running example and a Monte Carlo integration application.

KW - MPI

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Using RngStreams for parallel random number generation in C++ and R

Abstract

Keywords

ASJC Scopus subject areas

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