OK, I know the answer, but being inspired by this question, I'd like to get some nice opinions about the following: Why the Rcpp exercise below is ca. 15% faster (for long vectors) than the built-in exp()? We all know that Rcpp is a wrapper to the R/C API, so we should expect a slightly worse performance.
Rcpp::cppFunction("
NumericVector exp2(NumericVector x) {
NumericVector z = Rcpp::clone(x);
int n = z.size();
for (int i=0; i<n; ++i)
z[i] = exp(z[i]);
return z;
}
")
library("microbenchmark")
x <- rcauchy(1000000)
microbenchmark(exp(x), exp2(x), unit="relative")
## Unit: relative
## expr min lq median uq max neval
## exp(x) 1.159893 1.154143 1.155856 1.154482 0.926272 100
## exp2(x) 1.000000 1.000000 1.000000 1.000000 1.000000 100
Base R tends to do more checking for NA so we can win a little by not doing that. Also note that by doing tricks like loop unrolling (as done in Rcpp Sugar) we can do little better still.
So I added
Rcpp::cppFunction("NumericVector expSugar(NumericVector x) { return exp(x); }")
and with that I get a further gain -- with less code on the user side:
R> microbenchmark(exp(x), exp2(x), expSugar(x), unit="relative")
Unit: relative
expr min lq mean median uq max neval
exp(x) 1.11190 1.11130 1.11718 1.10799 1.08938 1.02590 100
exp2(x) 1.08184 1.08937 1.07289 1.07621 1.06382 1.00462 100
expSugar(x) 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 100
R>