Difference between revisions of "Rcpp"

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Revision as of 10:25, 12 July 2019

Misc

sudo apt-get install libblas-dev liblapack-dev
sudo apt-get install gfortran

Speed Comparison

  • A comparison of high-performance computing techniques in R. It compares Rcpp to an R looping operator (like mapply), a parallelized version of a looping operator (like mcmapply), explicit parallelization, via the parallel package or the ParallelR suite.
  • In the following example, C++ avoids the overhead of creating an intermediate object (eg vector of the same length as the original vector). The c++ uses an intermediate scalar. So C++ wins R over memory management in this case.
    # http://blog.mckuhn.de/2016/03/avoiding-unnecessary-memory-allocations.html 
    library(Rcpp)
    
    `%count<%` <- cppFunction('
    size_t count_less(NumericVector x, NumericVector y) {
      const size_t nx = x.size();
      const size_t ny = y.size();
      if (nx > 1 & ny > 1) stop("Only one parameter can be a vector!");
      size_t count = 0;
      if (nx == 1) {
        double c = x[0];
        for (int i = 0; i < ny; i++) count += c < y[i];
      } else {
        double c = y[0];
        for (int i = 0; i < nx; i++) count += x[i] < c;
      }
      return count;
    }
    ')
    
    set.seed(42)
    
    N <- 10^7
    v <- runif(N, 0, 10000)
    
    # Testing on my ODroid xu4 running ubuntu 15.10
    system.time(sum(v < 5000))
    #   user  system elapsed
    #  1.135   0.305   1.453
    system.time(v %count<% 5000)
    #   user  system elapsed
    #  0.535   0.000   0.540
    
  • Why R for data science – and not Python?
    library(Rcpp)
     
    bmi_R <- function(weight, height) {
      weight / (height * height)
    }
    bmi_R(80, 1.85) # body mass index of person with 80 kg and 185 cm
    ## [1] 23.37473
     
    cppFunction("
      float bmi_cpp(float weight, float height) {
        return weight / (height * height);
      }
    ")
    bmi_cpp(80, 1.85) # same with cpp function
    ## [1] 23.37473
    
  • Boost the speed of R calls from Rcpp

Use Rcpp in RStudio

RStudio makes it easy to use Rcpp package.

Open RStudio, click New File -> C++ File. It will create a C++ template on the RStudio editor

#include <Rcpp.h>
using namespace Rcpp;

// Below is a simple example of exporting a C++ function to R. You can
// source this function into an R session using the Rcpp::sourceCpp 
// function (or via the Source button on the editor toolbar)

// For more on using Rcpp click the Help button on the editor toolbar

// [[Rcpp::export]]
int timesTwo(int x) {
   return x * 2;
}

Now in R console, type

library(Rcpp)
sourceCpp("~/Downloads/timesTwo.cpp")
timesTwo(9)
# [1] 18

See more examples on http://adv-r.had.co.nz/Rcpp.html and Calculating a fuzzy kmeans membership matrix

If we wan to test Boost library, we can try it in RStudio. Consider the following example in stackoverflow.com.

// [[Rcpp::depends(BH)]]
#include <Rcpp.h>
#include <boost/foreach.hpp>
#include <boost/math/special_functions/gamma.hpp>

#define foreach BOOST_FOREACH

using namespace boost::math;

//[[Rcpp::export]]
Rcpp::NumericVector boost_gamma( Rcpp::NumericVector x ) {
  foreach( double& elem, x ) {
    elem = boost::math::tgamma(elem);
  };

  return x;
}

Then the R console

boost_gamma(0:10 + 1)
#  [1]       1       1       2       6      24     120     720    5040   40320
# [10]  362880 3628800

identical( boost_gamma(0:10 + 1), factorial(0:10) )
# [1] TRUE

Example 1. convolution example

First, Rcpp package should be installed (I am working on Linux system). Next we try one example shipped in Rcpp package.

PS. If R was not available in global environment (such as built by ourselves), we need to modify 'Makefile' file by replacing 'R' command with its complete path (4 places).

cd ~/R/x86_64-pc-linux-gnu-library/3.0/Rcpp/examples/ConvolveBenchmarks/
make
R

Then type the following in an R session to see how it works. Note that we don't need to issue library(Rcpp) in R.

dyn.load("convolve3_cpp.so")
x <- .Call("convolve3cpp", 1:3, 4:6)
x # 4 13 28 27 18

If we have our own cpp file, we need to use the following way to create dynamic loaded library file. Note that the character (grave accent) ` is not (single quote)'. If you mistakenly use ', it won't work.

export PKG_CXXFLAGS=`Rscript -e "Rcpp:::CxxFlags()"`
export PKG_LIBS=`Rscript -e "Rcpp:::LdFlags()"`
R CMD SHLIB xxxx.cpp

Example 2. Use together with inline package

library(inline)
src <-'
 Rcpp::NumericVector xa(a);
 Rcpp::NumericVector xb(b);
 int n_xa = xa.size(), n_xb = xb.size();

 Rcpp::NumericVector xab(n_xa + n_xb - 1);
 for (int i = 0; i < n_xa; i++)
 for (int j = 0; j < n_xb; j++)
 xab[i + j] += xa[i] * xb[j];
 return xab;
'
fun <- cxxfunction(signature(a = "numeric", b = "numeric"),
 src, plugin = "Rcpp")
fun(1:3, 1:4) 
# [1]  1  4 10 16 17 12

Example 3. Calling an R function

RcppParallel

rTRNG: parallel RNG in R

rTRNG: ADVANCED PARALLEL RNG IN R