Dear all,
This topic follows that one:
https://groups.google.com/forum/#!topic/julia-users/S86qxRkJ0ao which was
not extremely easy to read so I take the liberty to reformulate my problem
in simpler terms.
I want to compute a matrix row by row, and since these computations can be
done independently, on the advice of Tim Holy, I use SharedArrays, where
each worker is given a range of rows on which to perform a given operation.
The code is:
@everywhere begin
m = Int(1e3)
n = Int(5e3)
mat_b = rand(m, n)
function compute_row!(smat_a, mat_b, irange, n)
for i in irange
smat_a[i,i:n] = mean(mat_b[:,i] .* mat_b[:,i:n], 1)
end
end
end
smat_a = SharedArray(Float64, (n,n))
tic()
@sync begin
for p in procs(smat_a)
@async begin
irange = p-1:length(procs(smat_a)):n
remotecall_wait(p, compute_row!, smat_a, mat_b, irange, n)
end
end
end
toc()
And the problem is that it is always slower than the sequential version...
I have no idea why this does not work.
Any suggestion appreciated, thanks a lot!
