RcppArrayFire provides an Rcpp wrapper for the ArrayFire Library, an open source library that can make use of GPUs and other accelerators via CUDA or OpenCL.

Basic usage

Calculating \(\pi\) by simulation

Let’s look at the classical example of calculating \(\pi\) via simulation. The basic idea is to generate a large number of random points within the unit square. An approximation for \(\pi\) can then be calculated from the ratio of points within the unit circle to the total number of points. A vectorized implementation in R might look like this:

piR <- function(N) {
    x <- runif(N)
    y <- runif(N)
    4 * sum(sqrt(x^2 + y^2) < 1.0) / N

system.time(cat("pi ~= ", piR(10^7), "\n"))
#> pi ~=  3.140899
#>        user      system     elapsed 
#>       0.836       0.060       0.897

A simple way to use C++ code in R is to use the inline package or cppFunction() from Rcpp, which are both possible with RcppArrayFire. An implementation in C++ using ArrayFire might look like this:

src <- '
double piAF (const int N) {
    array x = randu(N, f32);
    array y = randu(N, f32);
    return 4.0 * sum<float>(sqrt(x*x + y*y) < 1.0) / N;
Rcpp::cppFunction(code = src, depends = "RcppArrayFire", includes = "using namespace af;")

system.time(cat("pi ~= ", piAF(10^7), "\n"))
#> pi ~=  3.141066
#>        user      system     elapsed 
#>       0.000       0.004       0.021

Several things are worth noting:

  1. The syntax is almost identical. Besides the need for using types and a different function name when generating random numbers, the argument f32 to randu as well as the float type catches the eye. These instruct ArrayFire to use single precision floats, since not all devices support double precision floating point numbers. If you want to use double precision, you have to specify f64 and double.

  2. The results are not the same, since ArrayFire uses a different random number generator.

  3. The speed-up can be quite impressive. However, sometimes the first invocation of a function is not as fast as expected due to the just-in-time compilation used by ArrayFire.

Arrays as parameters

Up to now we have only considered simple types like double or int as function parameters and return values. However, we can also use arrays. Consider the matrix product \(X^{\mathsf{T}} X\) for a random matrix \(X\) in R:

N <- 40
X <- matrix(rnorm(N * N * 2), ncol = N)
tXXR <- t(X) %*% X

The matrix multiplication can be implemented with RcppArrayFire using the appropriate matmul function:

src <- '
af::array squareMatrix(const RcppArrayFire::typed_array<f32>& x) {
    return af::matmulTN(x ,x);
Rcpp::cppFunction(code = src, depends = "RcppArrayFire")
tXXGPU <- squareMatrix(X)

all.equal(tXXR, tXXGPU)
#> [1] "Mean relative difference: 1.372856e-07"

Since an object of type af::array can contain different data types, the templated wrapper class RcppArrayFire::typed_array<> is used to indicate the desired data type when converting from R to C++. Again single precision floats are used with ArrayFire, which explains the difference between the two results. We can be sure that double precision is supported by switching the computation backend to “CPU”, which produces identical results:

src <- '
af::array squareMatrixF64(const RcppArrayFire::typed_array<f64>& x) {
    return af::matmulTN(x ,x);
Rcpp::cppFunction(code = src, depends = "RcppArrayFire")

tXXCPU <- squareMatrixF64(X)

all.equal(tXXR, tXXCPU)
#> [1] TRUE

Usage in a package

More serious functions should be defined in a permanent fashion. To facilitate this, RcppArrayFire contains the function RcppArraFire.package.skeleton(). This functions initialises a package with suitable configure script for linking with ArrayFire and RcppArrayFire. In order to implement new functionality you can then write C++ functions and save them in the src folder. Functions that should be callable from R should be marked with the [[Rcpp::export]] attribute. See the Rcpp vignettes on attributes and package writing for further details.