[Rcpp-devel] Sugar seems slower than Rcpp.

[Cedric Ginestet]

Dear All,

Here are some simulations that I have run this morning. Romain's 
suggestion to compute xV.size() before the loop and Douglas' idea of 
using accumulate appear to work best. However, both are substantially 
slower than the r-base function.

I have also included two more versions: (i) one similar to Romain's but 
using pre-incrementation**in the loop and (ii) one using the iterator in 
the loop. Another option may be to use the C++ boost library. I don't 
know if anyone on this list has experience with using boost.

See the results of the simulations below (N=1000 data sets).
Ced

#####################################################################
## Functions.
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")
Summing3 <- 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")
Summing4 <- cxxfunction(signature(x="numeric"), '
       NumericVector xV(x);
       return wrap(std::accumulate(xV.begin(), xV.end(), double()));
',plugin="Rcpp")
Summing5 <- 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")
Summing6 <- cxxfunction(signature(x="numeric"), '
       NumericVector xV(x);
       double out = 0.0;
       for(NumericVector::iterator i=xV.begin(); i!=xV.end(); ++i) out 
+= *i;
       return wrap(out);
',plugin="Rcpp")

#####################################################################
## Simulation: Time Testing.
n <- 1000000; N <- 1000
time.Sum <- matrix(0,N,7);
for(i in 1:N){
x <- rnorm(n)
time.Sum[i,1] <- system.time(Summing1(x))[3];
time.Sum[i,2] <- system.time(Summing2(x))[3];
time.Sum[i,3] <- system.time(Summing3(x))[3];
time.Sum[i,4] <- system.time(Summing4(x))[3];
time.Sum[i,5] <- system.time(Summing5(x))[3];
time.Sum[i,6] <- system.time(Summing6(x))[3];
time.Sum[i,7] <- system.time(sum(x))[3];
}# i
time.df <- data.frame(time.Sum)
names(time.df) <- 
c("Sugar","Rcpp","Rcpp_N","Accumulate","Pre-increment","Iterator","R")
boxplot(time.df)

#####################################################################
## RESULTS:
formatC(summary(time.df),dec=3)
      Sugar                 Rcpp                Rcpp_N
  " Min.   :0.01600  " " Min.   :0.01000  " "Min.   :0.005000  "
  " 1st Qu.:0.01600  " " 1st Qu.:0.01000  " "1st Qu.:0.005000  "
  " Median :0.01600  " " Median :0.01100  " "Median :0.006000  "
  " Mean   :0.01631  " " Mean   :0.01060  " "Mean   :0.005668  "
  " 3rd Qu.:0.01600  " " 3rd Qu.:0.01100  " "3rd Qu.:0.006000  "
  " Max.   :0.03700  " " Max.   :0.02400  " "Max.   :0.020000  "
    Accumulate         Pre-increment           Iterator
  "Min.   :0.005000  " "Min.   :0.005000  " " Min.   :0.01000  "
  "1st Qu.:0.005000  " "1st Qu.:0.005000  " " 1st Qu.:0.01000  "
  "Median :0.006000  " "Median :0.006000  " " Median :0.01100  "
  "Mean   :0.005714  " "Mean   :0.005697  " " Mean   :0.01065  "
  "3rd Qu.:0.006000  " "3rd Qu.:0.006000  " " 3rd Qu.:0.01100  "
  "Max.   :0.029000  " "Max.   :0.021000  " " Max.   :0.03100  "
        R
  "Min.   :0.002000  "
  "1st Qu.:0.002000  "
  "Median :0.002000  "
  "Mean   :0.002211  "
  "3rd Qu.:0.002000  "
  "Max.   :0.004000  "
#####################################################################

PS: Apologies to Dirk as I have not followed his advice, yet.

-- 
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


On 04/01/11 15:37, Dirk Eddelbuettel wrote:
> On 4 January 2011 at 15:14, Cedric Ginestet 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?
>
> 1)  Try to think a about measurement error here; these times are all minuscule.
>
> 2)  Consider reading the list archive, we have better use of benchmarks using
>      rbenchmark and replications; these are also some example in the examples
>      right in Rcpp
>
> 3)  Consider reading the list archive and discussions about the NoNA tests.
>
> 4)  Lastly, consider Romain's point about a baseline using an empty function.
>
> Dirk
>
>
> | 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
> |
> |
> | ----------------------------------------------------------------------
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> | Rcpp-devel mailing list
> | Rcpp-devel at lists.r-forge.r-project.org
> | https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
>
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