If you are writing some C++ code with the intent of calling it from R or even developing it into a package you might wonder whether it is better to use the pseudo random number library native to C++11 or the R standalone library. On the one hand users of your package might have an outdated compiler which doesn’t support C++11 but on the other hand perhaps there are potential speedups to be won by using the library native to C++11. I decided to compare the performance of these two libraries.

#define MATHLIB_STANDALONE
#include <iostream>
#include <vector>
#include <random>
#include <chrono>
#include "Rmath.h"

int main(int argc, char *argv[])
{
        int ndraws=100000000;
        std::vector<double> Z(ndraws);
        std::mt19937 engine;
        std::normal_distribution<double> N(0,1);

        auto start = std::chrono::steady_clock::now();
        for(auto & z : Z ) {
                z=N(engine);
        }
        auto end = std::chrono::steady_clock::now();
        std::chrono::duration<double> elapsed=end-start;

        std::cout <<  elapsed.count() << " seconds - C++11" << std::endl;

        start = std::chrono::steady_clock::now();
        for(auto & z : Z ) {
                z=rnorm(0,1);
        }
        end = std::chrono::steady_clock::now();
        elapsed=end-start;

        std::cout <<  elapsed.count() << " seconds - R Standalone" << std::endl;

        return 0;
}

Compiling and run with:

[michael@michael coda]$ g++ normal.cpp -o normal -std=c++11 -O3 -lRmath
[michael@michael coda]$ ./normal 

Normal Generation

5.2252 seconds - C++11
6.0679 seconds - R Standalone

Gamma Generation

11.2132 seconds - C++11
12.4486 seconds - R Standalone

Cauchy

6.31157 seconds - C++11
6.35053 seconds - R Standalone

As expected the C++11 implementation is faster but not by a huge amount. As the computational cost of my code is dominated by other linear algebra procedures of O(n^3) I’d actually be willing to use the R standalone library because the syntax is more user friendly.

The post C++11 versus R Standalone Random Number Generation Performance Comparison appeared first on Lindons Log.