[Rcpp-devel] How to increase the coding efficiency

[Christian Gunning]

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

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:



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