Hi, I started to work with Rcpp and I have some trouble to adapt some code. I copy below the following code:
- I want to write a function named contrib1, that is simplified version here, but the idea is that that function makes a large use of the pnorm (and qnorm) on marices and I would like to make it faster by applying the function using Rcpp parallel. - Then I started with an example that I tried to adapt (http://gallery.rcpp.org/articles/parallel-matrix-transform/) to build the structure Pnorm and the function pPnorm. I also created the function f (I wasn't able to use directly the pnorm function as would Sugar allow...) The example doesn't work. And I don't really understand why. I guess that it is a problem of memory allocation... Can anyone help me on that point?? Moreover, I was able to build a previous version that was working, but once I started to iterate on the contrib1 function (I execute an optimization algorithm), the RAM memory saturated I wasn't able to figure out why the program was not freeing memory after each execution of the function. I attach the code below. I am a Rcpp novice, so don't hesitate to comment on strange things.... Thanks for your answers! Maxime -------------------------------------- #include <RcppArmadillo.h> #include <cmath> #include <algorithm> #include <RcppParallel.h> using namespace Rcpp; using namespace arma; using namespace std; using namespace RcppParallel; // [[Rcpp::depends(RcppArmadillo, RcppParallel)]] inline double f(double x) { return ::Rf_pnorm5(x, 0.0, 1.0, 1, 0); } struct Pnorm : public Worker { // source matrix mat input; // destination matrix mat output; // initialize with source and destination Pnorm(mat input, mat output) : input(input), output(output) {} // take the square root of the range of elements requested void operator()(std::size_t begin, std::size_t end) { std::transform(input.begin() + begin, input.begin() + end, output.begin() + begin, f); } }; mat pPnorm(mat x) { // allocate the output matrix mat output(x.n_rows, x.n_cols); // Pnorm functor (pass input and output matrixes) Pnorm ppnorm(x, output); // call parallelFor to do the work parallelFor(0, x.n_elem, ppnorm); // return the output matrix mat outmat(output.begin(), output.n_rows, output.n_cols); return outmat; } // [[Rcpp::export]] mat contrib1(mat my_matrix) { mat pbound2 = pPnorm(my_matrix); return pbound2; } ------------------------------------------ ### Main R code rm(list=ls(all=TRUE)) library(Rcpp) library(RcppGSL) library(RcppParallel) library(RcppArmadillo) setwd('U:/testParallel') sourceCpp("test.cpp") asd = matrix(runif(1000), ncol = 100) contrib1(asd)
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