rTRNG: Advanced Parallel RNG in R

June 10, 2019
By

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Following on the recent CRAN release of
rTRNG, it’s
time to show its features and usage more in detail.


rTRNG

rTRNG is
a new package for advanced parallel Random Number Generation in R. It relies
on TRNG (Tina’s Random Number
Generator), a state-of-the-art C++ pseudo-random number generator library for
sequential and parallel Monte Carlo simulations. In particular, parallel
random number generators provided by TRNG can be manipulated by jump and
split operations. These allow to jump ahead by an arbitrary number of steps
and to split a sequence into any desired sub-sequence(s), thus enabling
techniques such as block-splitting and leapfrogging suitable to parallel
algorithms.

Background and Motivation

Monte Carlo simulations provide a powerful computational approach to address a
wide variety of problems in several domains, such as physical sciences,
engineering, computational biology and finance. The independent-samples and
large-scale nature of Monte Carlo simulations make the corresponding computation
suited for parallel execution, at least in theory. In practice, pseudo-random
number generators (RNGs) are intrinsically sequential. This often prevents
having a parallel Monte Carlo algorithm that is playing fair, meaning that
results are independent of the architecture, parallelization techniques and
number of parallel processes.

Fair-playing sub-simulation of a matrix of Monte Carlo variates

Advanced Parallel RNG in R

rTRNG provides the R user with access to the functionality of the underlying
TRNG C++ library. It makes use of
Rcpp and
RcppParallel
to expose the creation, manipulation and use of pseudo-random streams to R.
Moreover, the TRNG library and its headers are made available to other projects
combining R with C++.

An
introduction to rTRNG
(pdf)
was given at the useR!2017 conference, and is also available as
vignette("rTRNG.useR2017", "rTRNG").
A second vignette, vignette("mcMat", "rTRNG"), shows rTRNG in action for
the flexible and consistent (parallel) simulation of a matrix of Monte Carlo
variates, represented in the picture above.

The concrete application of such techniques to the simulation of credit defaults
was presented at the
R/Finance 2017
conference, showing how rTRNG can be used for fast sub-portfolio simulation,
risk-insight and scenario assessment. The code and data underlying this applied
use-case are hosted on
GitHub,
as is the corresponding
R Markdown output.

Below we illustrate, using simple examples, the several ways rTRNG can be
used.

Base-R-like usage

Similar to base-R (see ?Random), rTRNG allows to select and manipulate a
current TRNG generator of a given kind (e.g. yarn2), and to draw from it
using any of the provided r_trng functions:

library(rTRNG)
TRNGkind("yarn2") 
TRNGseed(12358)
runif_trng(15)
##  [1] 0.580259813 0.339434026 0.221393682 0.369402388 0.542678773
##  [6] 0.002851459 0.123996486 0.346813776 0.121799416 0.947124450
## [11] 0.336516569 0.128926181 0.380379891 0.550692382 0.436002654

The special jump and split operations can be applied to the current
generator in a similar way:

TRNGseed(12358)
TRNGjump(6) # advance by 6 the internal state
TRNGsplit(5, 3) # generate one element every 5 starting from the 3rd
runif_trng(2)
## [1] 0.1217994 0.5506924
#   => compare to the full sequence above
Direct manipulation of generators

Reference objects wrapping the underlying C++ TRNG generators can be created
and manipulated in OOP-style, for greater flexibility in using parallel RNGs in
R:

rng <- yarn2$new()
rng$seed(12358)
rng$jump(6)
rng$split(5, 3)
runif_trng(2, engine = rng)
## [1] 0.1217994 0.5506924
Parallel generation

Fair-playing, multi-threaded generation of random variates from R can be
anabled in r_trng via argument parallelGrain > 0, where the number of
parallel threads is controlled via RcppParallel::setThreadOptions():

TRNGseed(12358)
RcppParallel::setThreadOptions(numThreads = 2)
x_parallel <- runif_trng(1e5, parallelGrain = 100)
TRNGseed(12358)
x_serial <- runif_trng(1e5)
identical(x_serial, x_parallel)
## [1] TRUE
Standalone C++ code

The TRNG C++ library is made available by rTRNG to standalone C++ code
compiled with Rcpp::sourceCpp() thanks to the Rcpp::depends attribute:

// [[Rcpp::depends(rTRNG)]]
#include 
#include 
#include 
// [[Rcpp::export]]
Rcpp::NumericVector exampleCpp() {
  trng::yarn2 rng(12358);
  rng.jump(6);
  rng.split(5, 2); // 0-based index
  Rcpp::NumericVector x(2);
  trng::uniform_dist<>unif(0, 1);
  for (unsigned int i = 0; i < 2; i++) {
    x[i] = unif(rng);
  }
  return x;
}
exampleCpp()
## [1] 0.1217994 0.5506924
R packages

Creating an R package with C++ code using the TRNG library via rTRNG is
achieved by

  • adding Imports: rTRNG and LinkingTo: rTRNG to the DESCRIPTION file

  • importing one symbol in the NAMESPACE:
    importFrom(rTRNG, TRNG.Version)

  • setting the relevant linker flags in Makevars[.win] via rTRNG::LdFlags()

    • Makevars: PKG_LIBS += $(shell ${R_HOME}/bin/Rscript -e "rTRNG::LdFlags()")
    • Makevars.win: PKG_LIBS += $(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe" -e "rTRNG::LdFlags()")

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