[Rcpp-devel] Best way to compute M'M if M is triangular using RcppArmadillo
Ahmadou Dicko
dicko.ahmadou at gmail.com
Wed Apr 17 18:11:31 CEST 2013
I still don't get why you are copying the whole object.
But if you really want to initialize an array of the desired size, you can
use arma::copy_size
In you case it will be something like
........
mat prodM;
prodM.copy_size(M);
.........
On Wed, Apr 17, 2013 at 3:24 PM, F.Tusell <fernando.tusell at ehu.es> wrote:
> El mié, 17-04-2013 a las 14:01 +0000, Smith, Dale (Norcross) escribió:
> > I agree with Dirk. Take a look at the arma::mat constructor in fastLm.
> If you are not modifying M, use the raw memory constructor.
> >
> > http://gallery.rcpp.org/articles/fast-linear-model-with-armadillo/
> >
> > I don't see any other way to avoid using Rcpp::wrap in the return
> statement. Note there may be other places you copy memory, for instance, in
> Prod1 you write
> >
> > | mat prodM = M ;
> > | prodM = trans(M) * M ;
> >
>
> True. But in Prod3 I needed a pre-existent target array to store the
> result, so I defined it also in Prod1 and Prod2 to make things alike.
> Taking a copy of M is indeed a very clumsy way to initialize an array
> of the right size.
>
> > I think the first statement is not necessary.
> >
> > Dale Smith, Ph.D.
> > Senior Financial Quantitative Analyst
> > Risk & Compliance
> > Fiserv
> > Office: 678-375-5315
> > www.fiserv.com
> >
> >
> > -----Original Message-----
> > From: rcpp-devel-bounces at r-forge.wu-wien.ac.at [mailto:
> rcpp-devel-bounces at r-forge.wu-wien.ac.at] On Behalf Of Dirk Eddelbuettel
> > Sent: Wednesday, April 17, 2013 9:17 AM
> > To: fernando.tusell at ehu.es
> > Cc: rcpp-devel at r-forge.wu-wien.ac.at
> > Subject: Re: [Rcpp-devel] Best way to compute M'M if M is triangular
> using RcppArmadillo
> >
> >
> > On 17 April 2013 at 14:24, F.Tusell wrote:
> > | Not a question strictly about Rcpp but hope this is a right place to
> > | ask.
> > |
> > | I am trying to find out what is the fastest possible way to compute
> > | M'M for M upper triangular. I have coded,
> > |
> > | // [[Rcpp::export]]
> > | SEXP Prod1(SEXP M_) {
> > | const mat M = as<mat>(M_) ;
> > | mat prodM = M ;
> > | prodM = trans(M) * M ;
> > | return(wrap(prodM)) ;
> > | }
> > |
> > | // [[Rcpp::export]]
> > | SEXP Prod2(SEXP M_) {
> > | const mat M = as<mat>(M_) ;
> > | mat prodM = M ;
> > | prodM = trimatu(M).t() * trimatu(M) ;
> > | return(wrap(prodM)) ;
> > | }
> > |
> > |
> > | // [[Rcpp::export]]
> > | SEXP Prod3(SEXP M_) {
> > | mat M = as<mat>(M_) ;
> > | int d = M.n_rows ;
> > | mat prodM = M ;
> > | double * vM = M.memptr() ;
> > | double * vprodM = prodM.memptr() ;
> > | const double one = 1 ;
> > | const double zero = 0 ;
> > | F77_CALL(dsyrk)("L","T",&d,&d,&one,vM,&d,&zero,vprodM,&d) ;
> > | return(wrap(prodM)) ;
> > | }
> > |
> > | and then tested with:
> > |
> > | require(RcppArmadillo)
> > | require(microbenchmark)
> > | sourceCpp("prods.cpp")
> > | d <- 50
> > | a <- chol(crossprod(matrix(rnorm(d*d),d,d)))
> > |
> > | m <- microbenchmark(
> > | r1 <- Prod1(a),
> > | r2 <- Prod2(a),
> > | r3 <- Prod3(a),
> > | times=100
> > | )
> > |
> > | This is what I get:
> > |
> > | > m
> > | Unit: microseconds
> > | expr min lq median uq max neval
> > | r1 <- Prod1(a) 138.749 144.2260 148.4815 159.0830 2456.146 100
> > | r2 <- Prod2(a) 296.193 320.0275 329.4770 342.4185 2763.041 100
> > | r3 <- Prod3(a) 132.150 138.2590 140.9270 152.3675 218.719 100
> > |
> > | Prod3 using BLAS dsyrk is about as fast as Prod1, using Armadillo.
> > | I expected that telling Armadillo that M is upper triangular would
> > | make for a fastest product, and the contrary seems true; Prod2 takes
> > | about twice as much time as Prod1 and Prod3.
> > |
> > | Is there a faster way?
> >
> > You are going about this the right way by starting with empirics.
> >
> > Now, d=50 is not big so your (relative) savings will be rather small.
> Plus, the way you instantiate the Arma object DOES create extra copies on
> the arma object instantiation [ see discussion this list just last week ]
> as well as on exit -- which may dominate the timing. Try d=100, 200, ...
> 500 for comparison.
> >
> > Lastly, you could write prod3 without going to arma.
> >
> > Dirk
>
> I did try larger d, and to my surprise the relative timings stay about
> the same. Conrad S provides one piece of information I was missing, that
> trimatu() and trimatl() only have (as yet) effect with inv() and
> solve(), hence their use in Prod3 is just a waste of time. This explains
> the results.
>
> For the time being, I will stick with trans(M) * M. A function like R's
> crossprod() in Armadillo would be great. Thank you all for your help.
>
> ft.
>
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--
Ahmadou H. DICKO
statistician and applied economist
PhD student in Climate change economics
Faculty of economics and managment - Cheikh Anta Diop University
West African Science Service Center on Climate Change and Adaptated Land
Use (WASCAL)
Center for Development Research (ZEF) - University of Bonn
twitter : @dickoah
github : github/dickoa <https://github.com/dickoa>
tel : +221 33 827 55 16
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