[Rcpp-devel] OpenMP and Parallel BLAS

[Saurabh B]

Hi there,

I am using gradient descent to reduce a large matrix of users and items.
For this I am trying to use all 40 available cores but unfortunately my
performance is no better than when I was using just one. I am new to openMP
and RcppArmadillo so pardon my ignorance.

The main loop is -
#pragma omp parallel for for (int u = 0; u < C.n_rows; u++) { arma::mat Cu
= diagmat(C.row(u)); arma::mat YTCuIY = Y.t() * (Cu) * Y; arma::mat YTCupu
= Y.t() * (Cu + fact_eye) * P.row(u).t(); arma::mat WuT = YTY + YTCuIY +
lambda_eye; arma::mat xu = solve(WuT, YTCupu); // Update gradient -- maybe
a slow operation in parallel? X.row(u) = xu.t(); }


full code -
https://github.com/sanealytics/recommenderlabrats/blob/master/src/implicit.cpp

(implementing this paper -
http://www.researchgate.net/profile/Yifan_Hu/publication/220765111_Collaborative_Filtering_for_Implicit_Feedback_Datasets/links/0912f509c579ddd954000000.pdf
)

Matrices C, Y and P are large. Matrix X can be assumed to be small.

I have the following questions -
1) I have replaced my BLAS with OpenMP BLAS and am also using the "#pragma
omp parallel for" clause. Will they step over each other or are they
complimentary? I ask because my understanding is that the for loop will
split each user across threads, then the BLAS will redistribute the
matrices to multiply across all threads again. Is that right? And if so, is
that what we want to do?

2) Since the threads are running in parallel and I just need the resulting
value as output, I would ideally like a reduce() that gives each row in
sequence and I can construct the new X from it. I am not sure how to go
about doing that with Rcpp. I also want to avoid copying data as much as
possible.

Looking forward to your input,
Saurabh
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