dqrng

The dqrng package provides fast random number generators (RNG) with good statistical properties for usage with R. It combines these RNGs with fast distribution functions to sample from uniform, normal or exponential distributions. Both the RNGs and the distribution functions are distributed as C++ header-only library.

Table of Contents

Installation

The currently released version is available from CRAN via

install.packages(“dqrng”)

Intermediate releases can also be obtained via r-universe:

options(repos = c( rstub = ‘https://rstub.r-universe.dev’, CRAN = ‘https://cloud.r-project.org’)) install.packages(‘dqrng’)

Example

Using the provided RNGs from R is deliberately similar to using R’s build-in RNGs:

library(dqrng) dqset.seed(42) dqrunif(5, min = 2, max = 10) #> [1] 9.211802 2.616041 6.236331 4.588535 5.764814 dqrexp(5, rate = 4) #> [1] 0.35118613 0.17656197 0.06844976 0.16984095 0.10096744

They are quite a bit faster, though:

N <- 1e4 bm <- bench::mark(rnorm(N), dqrnorm(N), check = FALSE) bm[, 1:4] #> # A tibble: 2 × 4 #> expression min median `itr/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> #> 1 rnorm(N) 612µs 685.2µs 1397. #> 2 dqrnorm(N) 86µs 88.6µs 10388.

This is also true for the provided sampling functions with replacement:

m <- 1e7 n <- 1e5 bm <- bench::mark(sample.int(m, n, replace = TRUE), sample.int(1e3*m, n, replace = TRUE), dqsample.int(m, n, replace = TRUE), dqsample.int(1e3*m, n, replace = TRUE), check = FALSE) bm[, 1:4] #> # A tibble: 4 × 4 #> expression min median `itr/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> #> 1 sample.int(m, n, replace = TRUE) 6.88ms 7.63ms 114. #> 2 sample.int(1000 * m, n, replace = TRUE) 8.72ms 9.55ms 96.1 #> 3 dqsample.int(m, n, replace = TRUE) 482.21µs 810.29µs 1254. #> 4 dqsample.int(1000 * m, n, replace = TRUE) 492.79µs 822.86µs 1275.

And without replacement:

bm <- bench::mark(sample.int(m, n), sample.int(1e3*m, n), sample.int(m, n, useHash = TRUE), dqsample.int(m, n), dqsample.int(1e3*m, n), check = FALSE) #> Warning: Some expressions had a GC in every iteration; so filtering is #> disabled. bm[, 1:4] #> # A tibble: 5 × 4 #> expression min median `itr/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> #> 1 sample.int(m, n) 40.1ms 42.54ms 23.5 #> 2 sample.int(1000 * m, n) 12.19ms 14.38ms 67.8 #> 3 sample.int(m, n, useHash = TRUE) 9.43ms 11.17ms 81.9 #> 4 dqsample.int(m, n) 1.22ms 1.35ms 638. #> 5 dqsample.int(1000 * m, n) 1.98ms 2.51ms 358.

Note that sampling from 10^10 elements triggers “long-vector support”
in R.

It is also possible to use weighted sampling both with replacement:

m <- 1e6 n <- 1e4 prob <- dqrunif(m) bm <- bench::mark(sample.int(m, n, replace = TRUE, prob = prob), dqsample.int(m, n, replace = TRUE, prob = prob), check = FALSE) bm[, 1:4] #> # A tibble: 2 × 4 #> expression min median `itr/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> #> 1 sample.int(m, n, replace = TRUE, prob = prob) 22.02ms 23.82ms 41.7 #> 2 dqsample.int(m, n, replace = TRUE, prob = prob) 5.05ms 5.41ms 183.

And without replacement:

bm <- bench::mark(sample.int(m, n, prob = prob), dqsample.int(m, n, prob = prob), check = FALSE) bm[, 1:4] #> # A tibble: 2 × 4 #> expression min median `itr/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> #> 1 sample.int(m, n, prob = prob) 13.63s 13.63s 0.0734 #> 2 dqsample.int(m, n, prob = prob) 5.16ms 5.63ms 175.

Especially for weighted sampling without replacement the performance advantage compared with R’s default methods is particularly large.

In addition the RNGs provide support for multiple independent streams for parallel usage:

N <- 1e7 dqset.seed(42, 1) u1 <- dqrunif(N) dqset.seed(42, 2) u2 <- dqrunif(N) cor(u1, u2) #> [1] -0.0005787967

Feedback

All feedback (bug reports, security issues, feature requests, …) should be provided as issues.

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