RNGstreams {parallel} | R Documentation

## Implementation of Pierre L'Ecuyer's RngStreams

### Description

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

### 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.

### Author(s)

Brian Ripley

### References

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

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 1073–5.

### See Also

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

The vignette for package parallel.

### Examples

    RNGkind("L'Ecuyer-CMRG")
    set.seed(123)
    (s <- .Random.seed)
    ## do some work involving random numbers.
    nextRNGStream(s)
    nextRNGSubStream(s)

[Package parallel version 2.15.1 Index]