I have some experience in C++ and R but am a newbie to Rcpp. Recently I had a huge success by using Rcpp in some of my previous projects, so decided to apply it to a new project. I was surprised that my Rcpp code could be much slower than the corresponding R function. I have tried to simplify my R function to figure out the reason but cannot find any clue. Your help and comments are very welcome!
The main R function to compare R and Rcpp implementations:
main <- function(){
n <- 50000
Delta <- exp(rnorm(n))
delta <- exp(matrix(rnorm(n * 5), nrow = n))
rx <- matrix(rnorm(n * 20), nrow = n)
print(microbenchmark(c1 <- test(Delta, delta, rx), times = 500))
print(microbenchmark(c2 <- rcpp_test(Delta, delta, rx), times = 500))
identical(c1, c2)
list(c1 = c1, c2 = c2)
}
R implementation:
test <- function(Delta, delta, rx){
const <- list()
for(i in 1:ncol(delta)){
const[[i]] <- rx * (Delta / (1 + delta[, i]))
}
const
}
Rcpp implementation:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
List rcpp_test(NumericVector Delta,
NumericMatrix delta,
NumericMatrix rx) {
int n = Delta.length();
int m = rx.ncol();
List c;
NumericMatrix c1;
for(int i = 0; i < delta.ncol(); ++i){
c1 = NumericMatrix(n, m);
for(int k = 0; k < n; ++k){
double tmp = Delta[k] / (1 + delta(k, i));
for(int j = 0; j < c1.ncol(); ++j){
c1(k, j) = rx(k, j) * tmp;
}
}
c.push_back(c1);
}
return c;
}
I understand that there is no guarantee of improved efficiency by using Rcpp, but given the simple example I show here, I do not see why the Rcpp code runs so slowly.
Unit: milliseconds
expr min lq mean median uq max neval
c1 <- test(Delta, delta, rx) 13.16935 14.19951 44.08641 30.43126 73.78581 115.9645 500
Unit: milliseconds
expr min lq mean median uq max neval
c2 <- rcpp_test(Delta, delta, rx) 143.1917 158.7481 171.6116 163.413 173.7677 247.5495 500
Ideally rx is a list of matrices in my project. The variable i in the for loop will be used to pick an element for computing. At the beginning I suspected that passing a List to Rcpp could have a high overhead, so in this example, I assumed rx to be a fixed matrix being used for all i. Seems like that is not the reason for the slowness.
Your R code seems to be more or less optimal, i.e. all the real work is done in compiled code. For the C++ code the main problem I could find is calling c1.ncol() in a tight loop. If I replace that with m, the C++ solution is almost as fast as R. If I add RcppArmadillo to the mix, I get a very compact syntax, but not faster than the pure Rcpp code. To me this shows that it can be really hard to beat well written R code:
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
using namespace Rcpp;
// [[Rcpp::export]]
List arma_test(const arma::vec& Delta,
const arma::mat& delta,
const arma::mat& rx) {
int l = delta.n_cols;
List c(l);
for (int i = 0; i < l; ++i) {
c(i) = rx.each_col() % (Delta / (1 + delta.col(i)));
}
return c;
}
// [[Rcpp::export]]
List rcpp_test(NumericVector Delta,
NumericMatrix delta,
NumericMatrix rx) {
int n = Delta.length();
int m = rx.ncol();
List c(delta.ncol());
NumericMatrix c1;
for(int i = 0; i < delta.ncol(); ++i){
c1 = NumericMatrix(n, m);
for(int k = 0; k < n; ++k){
double tmp = Delta[k] / (1 + delta(k, i));
for(int j = 0; j < m; ++j){
c1(k, j) = rx(k, j) * tmp;
}
}
c(i) = c1;
}
return c;
}
/*** R
test <- function(Delta, delta, rx){
const <- list()
for(i in 1:ncol(delta)){
const[[i]] <- rx * (Delta / (1 + delta[, i]))
}
const
}
n <- 50000
Delta <- exp(rnorm(n))
delta <- exp(matrix(rnorm(n * 5), nrow = n))
rx <- matrix(rnorm(n * 20), nrow = n)
bench::mark(test(Delta, delta, rx),
arma_test(Delta, delta, rx),
rcpp_test(Delta, delta, rx))
*/
Output:
# A tibble: 3 x 14
expression min mean median max `itr/sec` mem_alloc n_gc n_itr
<chr> <bch:t> <bch:t> <bch:t> <bch:t> <dbl> <bch:byt> <dbl> <int>
1 test(Delt… 84.3ms 85.2ms 84.9ms 86.6ms 11.7 44.9MB 2 4
2 arma_test… 106.5ms 107.7ms 107.7ms 108.9ms 9.28 38.1MB 3 2
3 rcpp_test… 101.9ms 103.2ms 102.2ms 106.6ms 9.69 38.1MB 1 4
# … with 5 more variables: total_time <bch:tm>, result <list>, memory <list>,
# time <list>, gc <list>
I have also explicitly initialized the output list to the required size avoiding the push_back, but that did not make a big difference. With vector like data structures from Rcpp you should definitely avoid using push_back though, since there a copy will be made every time you extend the vector.