2013-01-06-using-the-rcpp-timer.md

title	author	updated	license	tags	summary	layout	src
Using the Rcpp Timer	Dirk Eddelbuettel	Dec 30, 2016	GPL (>= 2)	benchmark rng featured	This post shows how to use the Timer class in Rcpp	post	2013-01-06-using-the-rcpp-timer.cpp

Sine the 0.10.2 release, Rcpp contains an internal class Timer which can be used for fine-grained benchmarking. Romain motivated Timer in a post to the mailing list where Timer is used to measure the different components of the costs of random number generation.

A slightly modified version of that example follows below.

{% highlight cpp %} #include <Rcpp.h> #include <Rcpp/Benchmark/Timer.h>

using namespace Rcpp;

// [[Rcpp::export]] NumericVector useTimer() { int n = 1000000;

// start the timer
Timer timer;
timer.step("start");        // record the starting point

for (int i=0; i<n; i++) {
    GetRNGstate();
    PutRNGstate();
}
timer.step("get/put");      // record the first step

for (int i=0; i<n; i++) {
    GetRNGstate();
    rnorm(10, 0.0, 1.0);
    PutRNGstate();
}
timer.step("g/p+rnorm()");  // record the second step

for (int i=0; i<n; i++) {
    // empty loop
}
timer.step("empty loop");   // record the final step

NumericVector res(timer);   // 
for (int i=0; i<res.size(); i++) {
    res[i] = res[i] / n;
}
return res;

} {% endhighlight %}

We get the following result, each expressing the cost per iteration in nanoseconds, both cumulative (default) and incrementally (by taking differences).

{% highlight r %} res <- useTimer() res # normal results: cumulative {% endhighlight %}

      start     get/put g/p+rnorm()  empty loop 
   0.000156 1571.057133 3920.082384 3920.085223 

{% highlight r %} diff(res) # simple difference {% endhighlight %}

    get/put g/p+rnorm()  empty loop 
1571.056977 2349.025251    0.002839 

The interesting revelation is that repeatedly calling GetRNGstate() and PutRNGstate() can amount to about 60% of the cost of RNG draws. Luckily, we usually only have to call these helper functions once per subroutine called from R (rather than repeatedly as shown here) so this is not really a permanent cost to bear when running simulations with R.

It also show the usefulness of a fine-grained timer at the code level.