On 5 January 2012 at 11:45, Gregor Kastner wrote:
| hi again,
|
| I am currently in the process of speeding up my MCMC simulations and strongly
| consider using Rcpp. Since MCMC (naturally) heavily depends on fast RNG I was
| wondering whether anyone has tested the speed of syntactic sugar rnorm and
| friends as opposed to native C/C++ algorithms; or maybe can provide me with a
| reference?
What makes you think sugar is not native C++ wrapping native C? It really is
"just" a template layer around the native R API, implemented in C.
Generally speaking, this stuff is sometimes hard to figure out from code---so
we often suggest to run quick benchmarks, rather than conjecturing. We
really do believe in empirics here.
Run something like this:
library(inline)
library(rbenchmark)
nr <- function(N) rnorm(N)
nrcpp <- cxxfunction(signature(Ns="integer"), plugin="Rcpp", body='
int n = Rcpp::as<int>(Ns);
RNGScope tmp; // important for RNGs with Rcpp
return rnorm(n);
')
res <- benchmark(nr(10000), nrcpp(10000), order="relative")[,1:5]
print(res)
which on my system just showed a 20% gain (over a tight C function called
from R) :
edd@max:/tmp$ r gregor.r
Loading required package: methods
test replications elapsed relative user.self
2 nrcpp(10000) 100 0.086 1.000000 0.08
1 nr(10000) 100 0.104 1.209302 0.10
edd@max:/tmp$
If you can make it faster still let us know :)
The one thing you do NOT want to do is to call the R function rnorm via the
Rcpp::Function (or equivalent) interface: lots of overhead. Sugar, on the
other hand, is mean and lean.
Dirk
--
"Outside of a dog, a book is a man's best friend. Inside of a dog, it is too
dark to read." -- Groucho Marx
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