[Rcpp-devel] How to increase the coding efficiency

[Tim Triche, Jr.]

I have the book on my desk as it happens, and it is a great one, but the
appendix is better than I remembered, so I parceled it out:

http://www.flaver.com/BoydVandenberghe_appendixC_numericalLinearAlgebra.pdf
(hope this helps a few other people)

I bought the text because I like being able to write in the margins, but
I'd forgotten how good even the appendices are.



On Tue, Dec 4, 2012 at 7:01 PM, Kasper Daniel Hansen <
kasperdanielhansen at gmail.com> wrote:

> On Tue, Dec 4, 2012 at 9:55 PM, Christian Gunning <xian at unm.edu> wrote:
> > What exactly do these timings show?  A single call you your function?
> > How many calls?
> >
> > 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
>
> Very tangential, but I have always found appendix C of Boyd's convex
> optimization to be very useful on numerical linear algebra.  Freely
> available from the author at
>   http://www.stanford.edu/~boyd/cvxbook/
> (very nice book, but not something you read in an evening.  Appendix C
> is something that does not depend on anything else in the book
> though.)
>
> Kasper
>
> > 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!
> > _______________________________________________
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-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
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