Hi all,
I swear I tried to look into the documentation or online but I can't figure
out what I want to do.
I have a lot of sequential code executed and at some point I want to
parallelize the following loop:
mat_a = zeros(n, n)
for i = 1:n
mat_a[i,i:n] = mean(mat_b[:,i] .* mat_b[:,i:n], 1)
end
with mat_b being computed before.
I have a bunch of questions in order to better understand things:
- how to best choose the number of procs with which I run julia?
- since each operation on the rows of mat_a can be done independently from
the others, I'd like to send mat_b to each worker so that it can compute
certain lines of the matrix mat_a in the form of an array of vector which I
would concatenate afterwards to retrieve mat_a. I wanted to send mat_b with
the @everywhere macro but it seems this works only for definitions of
variables directly on a worker. I don't know how to send already computed
data to a specific worker.
- more generally, is this the best approach to parallelizing this kind of
code?
Any advice appreciated,
Thanks a lot,
