[Rcpp-devel] Problem passing Armadillo objects back to R

[baptiste auguie]

Hi,

I get the same incorrect results as Günter for both the <long> and
<int> versions.

Cheers,

baptiste

sessionInfo()
R version 2.12.2 Patched (2011-03-02 r54645)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  grid      methods
[8] base

other attached packages:
[1] RcppArmadillo_0.2.15 Rcpp_0.9.2           inline_0.3.8
[4] ggplot2_0.8.9        proto_0.3-8          reshape_0.8.3
[7] plyr_1.4

loaded via a namespace (and not attached):
[1] tools_2.12.2



On 8 March 2011 14:41, Dirk Eddelbuettel <edd at debian.org> wrote:
>
> Hi Guenter,
>
> On 7 March 2011 at 16:03, "Günter J. Hitsch" wrote:
> |
> | I'm new to Rcpp and RcppArmadillo---so far I like it a lot!  Thanks to the
> | developers for their good work.
> |
> | I run into a peculiar kind of problem when I try to pass a "large" Armadillo
> | object back to R.  Here's some code to replicate the problem in stylized form:
> |
> |
> | extern "C" SEXP testFun(SEXP L_)
> | {
> | const long L  = Rcpp::as<long>(L_);
> | arma::mat X(L,1);
> | arma::mat Y(L,1);
> | X.fill(1);
> | Y.fill(2);
> | return Rcpp::List::create(
> | Rcpp::Named("X") = Rcpp::wrap(X),
> | Rcpp::Named("Y") = Rcpp::wrap(Y)
> | );
> | }
> |
> |
> | I compile (both using g++ and the Intel Compiler --- choice of compiler makes
> | no difference) and then call from R:
> |
> |
> | L = 1000000
> | ret  = .Call("testFun", as.integer(L))
> | print(ret$X[1:5,])
> | print(ret$Y[1:5,])
> |
> |
> | Here's what I often get as output:
> |
> | [1] 1 1 1 1 1
> | [1] 1 1 1 1 1
> |
> | However, the second rows should be all 2's!
> |
> | When I try to pass smaller matrices, for example by setting L=100000, the
> | problem goes away:
> |
> | [1] 1 1 1 1 1
> | [1] 2 2 2 2 2
> |
> |
> | Also, the problem does not arise when I create and pass pack objects of type
> | Rcpp::NumericMatrix;  so far I've see the problem only with Armadillo objects.
> |  I've encountered this on two Macs running OS X 10.6.6, R 2.12.2, and I'm using
> | the latest versions of Rcpp and RcppArmadillo.
>
> I can't replicate that. I agree with you that we may have an issue here as
> for 'large' N (such as 500,000) I get a segfault which is definitely
> wrong (PS: but see below). On the other hand, for smaller values it works:
>
> R> require(inline)
> Loading required package: inline
> R>
> R> src <- '
> + const long L  = Rcpp::as<long>(ls);
> + arma::mat X(L,1);
> + arma::mat Y(L,1);
> + X.fill(1);
> + Y.fill(2);
> + return Rcpp::List::create(Rcpp::Named("X") = Rcpp::wrap(X),
> +                           Rcpp::Named("Y") = Rcpp::wrap(Y));'
> R>
> R> fun <- cxxfunction(signature(ls="numeric"), body=src, plugin="RcppArmadillo")
> R>
> R> head(as.data.frame(fun(5)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> R> head(as.data.frame(fun(500)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R> head(as.data.frame(fun(5000)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R> head(as.data.frame(fun(50000)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R>
>
>
> I quickly followed one hunch and the segfault is due to the long value for
> the index. If you use an int then even my (modest) 6gb ram machine is happy to
> allocate 50,000,000 objects. I stopped at that size.
>
> R> require(inline)
> Loading required package: inline
> R>
> R> src <- '
> + const int L  = Rcpp::as<int>(ls);
> + arma::mat X(L,1);
> + arma::mat Y(L,1);
> + X.fill(1);
> + Y.fill(2);
> + return Rcpp::List::create(Rcpp::Named("X") = Rcpp::wrap(X),
> +                           Rcpp::Named("Y") = Rcpp::wrap(Y));'
> R>
> R> fun <- cxxfunction(signature(ls="numeric"), body=src, plugin="RcppArmadillo")
> R>
> R> head(as.data.frame(fun(50000)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R> head(as.data.frame(fun(250000)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R> head(as.data.frame(fun(500000)))
>  X Y
> 1 1 2
> 2 1 2
> 3 1 2
> 4 1 2
> 5 1 2
> 6 1 2
> R> head(as.data.frame(fun(5000000)))
>  X Y
> 1 1 1
> 2 1 1
> 3 1 1
> 4 1 1
> 5 1 1
> 6 1 1
> R> head(as.data.frame(fun(50000000)))
>  X Y
> 1 1 1
> 2 1 1
> 3 1 1
> 4 1 1
> 5 1 1
> 6 1 1
> R>
>
> Dirk
>
> --
> Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
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