Le 04/01/11 17:42, Romain Francois a écrit :
Le 04/01/11 16:35, Andrew Redd a écrit :
Cedric,
This was addressed about 2 weeks ago on the list, please read the
archives. Basic point, Rcpp/sugar will not be faster than basic
functions in R. Do something more complicated to see improvements. I
would suggest something that involves lots of copies and edits, that
will mess with R's memory management but be much more efficient in
compiled code.
-Andrew
Hmmm. I suspect someone might be able to speed up "sum". Essentially it
contains lots of tests for NA that might be expensive if none of the
input data is NA.
Not sure I want to be that someone just yet.
Also, you might want to consider calculating xV.size() once (out of the
loop) instead of xV.size() times.
So for example:
Summing2 <- cxxfunction(signature(x="numeric"), '
NumericVector xV(x);
double out = 0.0;
int n = xV.size() ;
for(int i=0; i<n; i++) out += xV[i];
return wrap(out);
',plugin="Rcpp")
For me this is faster than R:
> n <- 10000000; x <- rnorm(n)
> system.time(Summing1(x));
utilisateur système écoulé
0.040 0.000 0.039
> system.time(Summing2(x));
utilisateur système écoulé
0.011 0.000 0.011
> system.time(sum(x));
utilisateur système écoulé
0.017 0.000 0.017
Someone will deal with the sugar version later.
Romain
On Tue, Jan 4, 2011 at 8:14 AM, Cedric Ginestet
<c.gineste...@googlemail.com <mailto:c.gineste...@googlemail.com>> wrote:
Happy new year to everyone,
I have made a very straightforward comparison of the performance of
standard R, Rcpp function and sugar, and found that the latter
produces the poorest performance. Let me know what you think and how
I could improve such performance assessment.
###################################################
Summing1 <- cxxfunction(signature(x="numeric"), '
NumericVector xV(x);
double out = sum(xV);
return wrap(out);
',plugin="Rcpp")
Summing2 <- cxxfunction(signature(x="numeric"), '
NumericVector xV(x);
double out = 0.0;
for(int i=0; i<xV.size(); i++) out += xV[i];
return wrap(out);
',plugin="Rcpp")
###################################################
# Results.
n <- 1000000; x <- rnorm(n)
Summing1(x); Summing2(x); sum(x)
#######################
gives:
[1] -396.6129
[1] -396.6129
[1] -396.6129
###################################################
# Time.
system.time(Summing1(x)); # Sugar
system.time(Summing2(x)); # Rcpp
system.time(sum(x)); # R-base
###################
> system.time(Summing1(x));
user system elapsed
0.016 0.000 0.016
> system.time(Summing2(x));
user system elapsed
0.008 0.000 0.011
> system.time(sum(x));
user system elapsed
0.000 0.000 0.003
Sugar appears to be the slowest! What about Rcpp basic loop? Why
isn't as fast as the standard sum() in R-base?
Cheers,
Cedric
--
Cedric Ginestet
Centre for Neuroimaging Sciences (L3.04)
NIHR Biomedical Research Centre
Institute of Psychiatry, Box P089
Kings College London
De Crespigny Park
London
SE5 8AF
--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
http://romainfrancois.blog.free.fr
|- http://bit.ly/fT2rZM : highlight 0.2-5
|- http://bit.ly/gpCSpH : Evolution of Rcpp code size
`- http://bit.ly/hovakS : RcppGSL initial release
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