On Tue, Dec 4, 2012 at 9:55 PM, Christian Gunning <x...@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! > _______________________________________________ > Rcpp-devel mailing list > Rcpp-devel@lists.r-forge.r-project.org > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel _______________________________________________ Rcpp-devel mailing list Rcpp-devel@lists.r-forge.r-project.org https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
