Flexible and consistent simulation of a matrix of Monte Carlo variates

knitr::opts_chunk$set(collapse = TRUE)
options(digits = 5) # for kable
linking_ok <- rTRNG::check_rTRNG_linking()
source("utils/read_chunk_wrap.R", echo = FALSE, print.eval = FALSE)
read_chunk_wrap("code/mcMat.R")
read_chunk_wrap("code/mcMat.cpp")
read_chunk_wrap("code/mcMatParallel.cpp")
if (linking_ok) {
  Rcpp::sourceCpp("code/mcMat.cpp", verbose = FALSE, embeddedR = FALSE)
  Rcpp::sourceCpp("code/mcMatParallel.cpp", verbose = FALSE, embeddedR = FALSE)
}

Consider the Monte Carlo simulation of a matrix of i.i.d. normal random variables. We will show how rTRNG can be used to perform a consistent (fair-playing) simulation of a subset of the variables and simulations.

{ width=85% }

Consistent simulation in R

We rely on the TRNG engines exposed to R as reference classes by rTRNG.

library(rTRNG)

The mcMatR function below performs the full sequential Monte Carlo simulation of nrow normal i.i.d. samples of ncol variables using the yarn2 generator.


A second function mcSubMatR relies on jump and split operations to perform only a chunk [startRow, endRow] of simulations for a subset subCols of the variables.


The parallel nature of the yarn2 generator ensures the sub-simulation obtained via mcSubMatR is consistent with the full sequential simulation.


knitr::kable(cbind.data.frame(M = M, S = S), row.names = TRUE)

Consistent simulation with Rcpp

We now use Rcpp to define functions mcMatRcpp and mcSubMatRcpp for the full sequential simulation and the sub-simulation, respectively. The Rcpp::depends attribute makes sure the TRNG library and headers shipped with rTRNG are available to the C++ code. Moreover, Rcpp::plugins(cpp11) enforces the C++11 standard required by TRNG >= 4.22.

```{Rcpp depends-h-ns, eval=FALSE}

```{Rcpp mcMatRcpp, eval=FALSE}

```{Rcpp mcSubMatRcpp, eval=FALSE}

As seen above for the R case, consistency of the simulation obtained via
`mcSubMatRcpp` with the full sequential simulation is guaranteed.
```r
knitr::kable(cbind.data.frame(M = M, S = S), row.names = TRUE)

Consistent parallel simulation with RcppParallel

The same technique used for generating a sub-set of the simulations can be exploited for performing a parallel simulation in C++. We can embed the body of mcSubMatRcpp above into an RcppParallel::Worker for performing chunks of Monte Carlo simulations in parallel, for any subset subCols of the variables. ```{Rcpp mcMatRcppParallel, eval=FALSE}

The parallel nature of the `yarn2` generator ensures the parallel simulation is
playing fair, i.e. is consistent with the sequential simulation.
```r

Similarly, we can achieve a consistent parallel simulation of a subset of the variables only.


knitr::kable(cbind.data.frame(M = M, Sp = Sp), row.names = TRUE)


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rTRNG documentation built on Jan. 31, 2021, 1:07 a.m.