Groups   51 of 99+      julia-users › Parallel loop 3 posts by 2 authors      ami... gmail.com    10 22 15   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,     Tim Holy    10 22 15   In short: use SharedArrays, if all processes are on the same host. The example  in the documentation should make this pretty clear, but feel free to post  again if it's not.   As for   of procs, just try different choices up to the   of cores in your  machine and see what happens.   --Tim       ami... gmail.com    10 23 15   Thanks a lot for this hint Tim. I think this is indeed exactly what I need. I have implemented it more or less successfully in the sense that it works and computes the correct matrix:     parallel helpers, from there: http:  stackoverflow.com questions 27677399 julia-how-to-copy-data-to-another-processor-in-julia  function sendto p::Int; args...      for  nm, val  in args          spawnat p, eval Main, Expr :   , nm, val        end end  function sendto ps::Vector Int ; args...      for p in ps         sendto p; args...      end end  getfrom p::Int, nm::Symbol; mod Main    fetch  spawnat p, getfield mod, nm     function passobj src::Int, target::Vector Int , nm::Symbol;                  from_mod Main, to_mod Main      r   RemoteRef src       spawnat src, put! r, getfield from_mod, nm        for to in target          spawnat to, eval to_mod, Expr :   , nm, fetch r         end     nothing end  function passobj src::Int, target::Int, nm::Symbol; from_mod Main, to_mod Main      passobj src,  target , nm; from_mod from_mod, to_mod to_mod  end  function passobj src::Int, target, nms::Vector Symbol ;                  from_mod Main, to_mod Main      for nm in nms         passobj src, target, nm; from_mod from_mod, to_mod to_mod      end end     variables  m   Int 1e3  n   Int 1e4  mat_b   rand m, n      sequential  function compute_row_sequential mat_b, i, n      return mean mat_b :,i  .  mat_b :,i:n , 1  end  mat_a   zeros n, n   tic    for i   1:n     mat_a i,i:n    compute_row_sequential mat_b, i, n  end toc       parallel  addprocs 3    everywhere function compute_row_shared! 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  sendto workers  , n   n  sendto workers  , mat_b   mat_b   smat_a   SharedArray Float64,  n,n , pids   workers     tic    sync begin     for p in procs smat_a           async begin             irange   p-1:length procs smat_a  :n             remotecall_wait p, compute_row_shared!, smat_a, mat_b, irange, n          end     end end toc    println mat_a    smat_a   the last line returns true, but I tried different values of m and n and I could not find a case where the parallel implementation is more efficient than the sequential one. So obviously, either I'm doing something wrong or it's not the best approach for this case...  I'll keep on trying to improve the efficiency, any advice again welcome :   Thank you.