I agree that RcppEigen is a little bit faster, but ease of use is important
to me, so I feel like RcppArmadillo might win out in my application.
| RcppArmadillo will use the very same LAPACK and BLAS libs your R session
| uses. So MKL, OpenBlas, ... are all options. Eigen actually has its own
code
| outperforming LAPACK, so it doesn't as much there.
Why do you think R outperforms RcppArmadillo in this example below? Anyway
to speed this up?
require(RcppArmadillo)
require(inline)
arma.code <- '
using namespace arma;
NumericMatrix Xr(Xs);
int n = Xr.nrow(), k = Xr.ncol();
mat X(Xr.begin(), n, k, false);
mat U;
vec s;
mat V;
svd(U, s, V, X);
return wrap(s);
'
rcppsvd <- cxxfunction(signature(Xs="numeric"),
arma.code,
plugin="RcppArmadillo")
A<-matrix(rnorm(5000^2), 5000)
> system.time(rcppsvd(A))
user system elapsed
1992.406 4.862 1988.737
> system.time(svd(A))
user system elapsed
652.496 2.641 652.614
On Wed, Jun 13, 2012 at 11:43 AM, Dirk Eddelbuettel <e...@debian.org> wrote:
>
> On 13 June 2012 at 10:57, Julian Smith wrote:
> | I've been toying with both RcppArmadillo and RcppEigen the past few days
> and
> | don't know which library to continue using. RcppEigen seems really
> slick, but
> | appears to be lacking some of the decompositions I want and isn't nearly
> as
> | fast to code. RcppArmadillo seems about as fast, easier to code up etc.
> What
> | are some of the advantages/disadvantages of both?
>
> That's pretty close. I have been a fan of [Rcpp]Armadillo which I find
> easier to get my head around. Doug, however, moved from [Rcpp]Armadillo to
> [Rcpp]Eigen as it has some things he needs. Eigen should have a "larger"
> API
> than Armadillo, but I find the code and docs harder to navigate.
>
> And you should find Eigen to be a little faster. Andreas Alfons went as far
> as building 'robustHD' using RcppArmadillo with a drop-in for RcppEigen (in
> package 'sparseLTSEigen'; both package names from memmory and I may have
> mistyped). He reported a performance gain of around 25% for his problem
> sets. On the 'fastLm' benchmark, we find the fast Eigen-based
> decompositions
> to be much faster than Armadillo.
>
> | Can you call LAPACK or BLAS from either? Is there a wrapper in RcppEigen
> to
> | call LAPACK functions? Want some other decomposition methods, dont like
> the
> | JacobiSVD method in Eigen.
>
> You need to differentiate between the Eigen and Armadillo docs _for their
> libraries_ and what happens when you access the Rcpp* variant from R.
>
> RcppArmadillo will use the very same LAPACK and BLAS libs your R session
> uses. So MKL, OpenBlas, ... are all options. Eigen actually has its own
> code
> outperforming LAPACK, so it doesn't as much there.
>
> Hope this helps, Dirk (at useR!)
>
> |
> | ----------------------------------------------------------------------
> | _______________________________________________
> | Rcpp-devel mailing list
> | Rcpp-devel@lists.r-forge.r-project.org
> | https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
> --
> Dirk Eddelbuettel | e...@debian.org | http://dirk.eddelbuettel.com
>
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