> Indeed, changing the sample size from 5 to 500 results in;
For completeness, I've included an iterator example, tested over a
range of sample sizes, and plotted the results with the code below.
From where I sit, it looks like the relative timings flatten out (more
or less) at 1e4.
I'm not sure the norm_rand is really an apples-to-apples comparison,
since this function call may be incrementally faster due to lack of
arguments.
Honestly, this is a testament to how well the sugar code works, if you
accept that it does in fact work :)
-Christian
library(inline)
library(Rcpp)
library(rbenchmark)
library(plyr)
library(lattice)
fun1 <- cxxfunction(signature(N="integer"), body = '
int NN = as<int>(N);
RNGScope scope;
NumericVector rn(NN);
for (int i = 0; i < NN; i++) {
rn[i] = as<double>(rnorm(1, 0.0, 1.0));
}
return rn;
', plugin = "Rcpp")
fun2 <- cxxfunction(signature(N="integer"), body = '
int NN = as<int>(N);
RNGScope scope;
NumericVector rn(NN);
for (int i = 0; i < NN; i++) {
rn[i] = Rf_rnorm(0.0, 1.0);
}
return rn;
', plugin = "Rcpp")
fun3 <- cxxfunction(signature(N="integer"), body = '
int NN = as<int>(N);
RNGScope scope;
NumericVector rn(NN);
for (int i = 0; i < NN; i++) {
rn[i] = norm_rand();
}
return rn;
', plugin = "Rcpp")
fun4 <- cxxfunction(signature(N="integer"), body = '
int NN = as<int>(N);
RNGScope scope;
return rnorm(NN, 0.0, 1.0);
', plugin = "Rcpp")
fun5 <- cxxfunction(signature(N="integer"), body = '
int NN = as<int>(N);
RNGScope scope;
NumericVector rn(NN);
std::generate( rn.begin(), rn.end(), RandomNumber);
return rn; ',
includes='
inline double RandomNumber () { return
Rf_rnorm(0.0, 1.0); }',
plugin = "Rcpp")
test.N = 4
set.seed(1)
print(fun1(test.N))
set.seed(1)
print(fun2(test.N))
set.seed(1)
print(fun3(test.N))
set.seed(1)
print(fun4(test.N))
set.seed(1)
print(fun5(test.N))
results <- ldply(c(10^(1:5)), function(N) {
ret <- benchmark(fun1(N), fun2(N), fun3(N), fun4(N), fun5(N),
order = "relative", replications = 1e3L)
ret$N <- N
ret
}, .progress='text')
results$test = factor(results$test, labels=c("Loop, as<>", "Loop,
Rf_rnorm", "Loop, norm_rand", "Sugar", "Iterator"))
plot(
xyplot( relative ~ N, groups=test, auto.key=list(space='right'),
type=c('l','g', 'p'), data=results,
scales=list(x=list(log=T)))
)
--
A man, a plan, a cat, a ham, a yak, a yam, a hat, a canal – Panama!
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