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! >
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