Rcpp function to be SLOWER than same R function - c++

Question

I have been coding a R function to compute an integral with respect to certain distributions, see code below.
EVofPsi = function(psi, probabilityMeasure, eps=0.01, ...){
distFun = function(u){
probabilityMeasure(u, ...)
}
xx = yy = seq(0,1,length=1/eps+1)
summand=0
for(i in 1:(length(xx)-1)){
for(j in 1:(length(yy)-1)){
signPlus = distFun(c(xx[i+1],yy[j+1]))+distFun(c(xx[i],yy[j]))
signMinus = distFun(c(xx[i+1],yy[j]))+distFun(c(xx[i],yy[j+1]))
summand = c(summand, psi(c(xx[i],yy[j]))*(signPlus-signMinus))
}
}
sum(summand)
}
It works fine, but it is pretty slow. It is common to hear that re-programming the function in a compiled language such as C++ would speed it up, especially because the R code above involves a double loop. So did I, using Rcpp:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
double EVofPsiCPP(Function distFun, Function psi, int n, double eps) {
NumericVector xx(n+1);
NumericVector yy(n+1);
xx[0] = 0;
yy[0] = 0;
// discretize [0,1]^2
for(int i = 1; i < n+1; i++) {
xx[i] = xx[i-1] + eps;
yy[i] = yy[i-1] + eps;
}
Function psiCPP(psi);
Function distFunCPP(distFun);
double signPlus;
double signMinus;
double summand = 0;
NumericVector topRight(2);
NumericVector bottomLeft(2);
NumericVector bottomRight(2);
NumericVector topLeft(2);
// compute the integral
for(int i=0; i<n; i++){
//printf("i:%d \n",i);
for(int j=0; j<n; j++){
//printf("j:%d \n",j);
topRight[0] = xx[i+1];
topRight[1] = yy[j+1];
bottomLeft[0] = xx[i];
bottomLeft[1] = yy[j];
bottomRight[0] = xx[i+1];
bottomRight[1] = yy[j];
topLeft[0] = xx[i];
topLeft[1] = yy[j+1];
signPlus = NumericVector(distFunCPP(topRight))[0] + NumericVector(distFunCPP(bottomLeft))[0];
signMinus = NumericVector(distFunCPP(bottomRight))[0] + NumericVector(distFunCPP(topLeft))[0];
summand = summand + NumericVector(psiCPP(bottomLeft))[0]*(signPlus-signMinus);
//printf("summand:%f \n",summand);
}
}
return summand;
}
I'm pretty happy since this C++ function works fine. However, when I tested both functions, the C++ one ran SLOWER:
sourceCpp("EVofPsiCPP.cpp")
pFGM = function(u,theta){
u[1]*u[2] + theta*u[1]*u[2]*(1-u[1])*(1-u[2])
}
psi = function(u){
u[1]*u[2]
}
print(system.time(
for(i in 1:10){
test = EVofPsi(psi, pFGM, 1/100, 0.2)
}
))
test
print(system.time(
for(i in 1:10){
test = EVofPsiCPP(psi, function(u){pFGM(u,0.2)}, 100, 1/100)
}
))
So, is there some kind expert around willing to explain me this? Did I code like a monkey and is there a way to speed up that function? Moreover, I would have a second question. Indeed, I could have replaced the output type double by SEXP, and the argument types Function by SEXP as well, it doesn't seem to change anything. So what is the difference?
Thank you very much in advance,
Gildas

Answer 1

Others have answered in comments already. So I'll just emphasize the point: Calling back to R functions is expensive as we need to be extra cautious about error handling. Just having the loop in C++ and call R functions is not rewriting your code in C++. Try rewriting psi and pFGM as C++ functions and report back here what happens.
You might argue that you lose some flexibility and you're not able anymore to use any R function. For situations like this, I'd advise to use some sort of hybrid solution where you have implemented the most common cases in C++ and fallback to an R solution otherwise.
As for the other question, a SEXP is an R object. This is part of the R API. It can be anything. When you create a Function from it (as is done implicitly for you when create a function that takes a Function argument), you are guaranteed that this is indeed an R function. The overhead is very small, but the gain in terms of expressiveness of your code is huge.