# RNGstreams

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##### Implementation of Pierre L'Ecuyer's RngStreams

This is an R re-implementation of Pierre L'Ecuyer's ‘RngStreams’ multiple streams of pseudo-random numbers.

Keywords
distribution, sysdata
##### Usage
nextRNGStream(seed)
nextRNGSubStream(seed)clusterSetRNGStream(cl = NULL, iseed)
mc.reset.stream()
##### Arguments
seed

An integer vector of length 7 as given by .Random.seed when the "L'Ecuyer-CMRG" RNG is in use. See RNG for the valid values.

cl

A cluster from this package or package snow, or (if NULL) the registered cluster.

iseed

An integer to be supplied to set.seed, or NULL not to set reproducible seeds.

##### Details

The ‘RngStream’ interface works with (potentially) multiple streams of pseudo-random numbers: this is particularly suitable for working with parallel computations since each task can be assigned a separate RNG stream.

This uses as its underlying generator RNGkind("L'Ecuyer-CMRG"), of L'Ecuyer (1999), which has a seed vector of 6 (signed) integers and a period of around $2^{191}$. Each ‘stream’ is a subsequence of the period of length $2^{127}$ which is in turn divided into ‘substreams’ of length $2^{76}$.

The idea of L'Ecuyer et al (2002) is to use a separate stream for each of the parallel computations (which ensures that the random numbers generated never get into to sync) and the parallel computations can themselves use substreams if required. The original interface stores the original seed of the first stream, the original seed of the current stream and the current seed: this could be implemented in R, but it is as easy to work by saving the relevant values of .Random.seed: see the examples.

clusterSetRNGStream selects the "L'Ecuyer-CMRG" RNG and then distributes streams to the members of a cluster, optionally setting the seed of the streams by set.seed(iseed) (otherwise they are set from the current seed of the master process: after selecting the L'Ecuyer generator).

Calling mc.reset.stream() after setting the L'Ecuyer random number generator and seed makes runs from mcparallel(mc.set.seed = TRUE) reproducible. This is done internally in mclapply and pvec. (Note that it does not set the seed in the master process, so does not affect the fallback-to-serial versions of these functions.)

##### Value

For nextRNGStream and nextRNGSubStream, a value which can be assigned to .Random.seed.

##### Note

Interfaces to L'Ecuyer's C code are available in CRAN packages rlecuyer and rstream.

##### References

L'Ecuyer, P. (1999). Good parameters and implementations for combined multiple recursive random number generators. Operations Research, 47, 159--164. 10.1287/opre.47.1.159.

L'Ecuyer, P., Simard, R., Chen, E. J. and Kelton, W. D. (2002). An object-oriented random-number package with many long streams and substreams. Operations Research, 50, 1073--1075. 10.1287/opre.50.6.1073.358.

RNG for fuller details of R's built-in random number generators.

The vignette for package parallel.

##### Aliases
• nextRNGStream
• nextRNGSubStream
• clusterSetRNGStream
• mc.reset.stream
##### Examples
library(parallel) # NOT RUN { RNGkind("L'Ecuyer-CMRG") set.seed(123) (s <- .Random.seed) ## do some work involving random numbers. nextRNGStream(s) nextRNGSubStream(s) # } 
Documentation reproduced from package parallel, version 3.6.2, License: Part of R 3.6.2

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