[Rcpp-devel] How to increase the coding efficiency

[Honglang Wang]

Yes, the main issue for my coding is the allocation of memory.  And I have
fixed one of the biggest memory allocation issue: 4000 by 4000 diagonal
matrix. And since I am not familiar with Rcpp and RcppArmadillo, I have no
idea how to reuse the memory. I hope I can have some materials to learn
this. Thanks.



> What exactly do these timings show?  A single call you your function?
> How many calls?
>
> Here I called my function for 100 times.


> Building on Romain's point: -- a portion of your function's runtime is
> in memory allocation
> (and you have a lot of allocations here).
> If you're calling your function thousands or millions of times, then
> it might pay to closely
> examine your memory allocation strategies and figure out what's
> temporary, for example.
> It looks like you're already using  copy_aux_mem = false in a number
> of places, but you're
> allocating a lot of objects -- of approx what size?
>
> For example, wouldn't this work just as well with one less allocation?
> arma::vec kk = t;
> arma::uvec q1 = arma::find(arma::abs(tp)<h);
> kk.elem(q1) = ((1-arma::pow(tp.elem(q1)/h,2))/h)*0.75;
> // done with q1.  let's reuse it.
> q1 = arma::find(arma::abs(tp)>=h);
> // was q2
> kk.elem(q1).zeros();
>
> You could potentially allocate memory for temporary working space in
> R, grab it with copy_aux_mem = false, write your temp results there,
> and reuse these objects in subsequent function calls.  It doesn't make
> sense to go to this trouble, though, if your core algorithm consumes
> the bulk of runtime.
>
> Have you looked on the armadillo notes r.e. inv?  Matrix inversion has
> O(>n^2).  You may be aided by pencil-and-paper math here.
> http://arma.sourceforge.net/docs.html#inv
>
> Here my matrix for inverse is only 4 by 4, so I think it's ok.


> best,
> Christian
>
> > Dear All,
> > I have tried out the first example by using RcppArmadillo, but I am not
> > sure whether the code is efficient or not. And I did the comparison of
> the
> > computation time.
> >
> > 1) R code using for loop in R: 87.22s
> > 2) R code using apply: 77.86s
> > 3) RcppArmadillo by using for loop in C++: 53.102s
> > 4) RcppArmadillo together with apply in R: 47.310s
> >
> > It is kind of not so big increase. I am wondering whether I used an
> > inefficient way for the C++ coding:
>
>
>
> --
> A man, a plan, a cat, a ham, a yak, a yam, a hat, a canal – Panama!
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.r-forge.r-project.org/pipermail/rcpp-devel/attachments/20121204/d1c99d04/attachment.html>