The "@parallel for" works only with ranges - only data that is in the for
body is copied. We should print a better error message though.
I cannot think of a way to have a distributed randperm that does not
involve copying other than using a SharedArray.
If it is not an issue copying only the specific parts of the distribution,
a DArray can also serve your purpose.
n=1000
x = randperm(n); y = randperm(n)
d=distribute(map(t->t, zip(x,y)))
# Only the specific localparts are copied to each of the workers
participating in the darray...
@sync begin
for p in procs(d)
@async begin
remotecall_fetch(p,
D -> begin
for t in localpart(D)
println(t)
# do any work on the localpart of the DArray
end
end,
d)
end
end
end
On Sun, Jan 26, 2014 at 11:54 PM, Madeleine Udell <[email protected]
> wrote:
> @parallel breaks when paralleling a loop over a Zip. Is there a workaround
> that allows me not to explicitly form the sequence I'm iterating over? I'd
> like to avoid copying (unnecessarily) the data from the sequences I'm
> zipping up.
>
> n = 1000
> x = randperm(n); y = randperm(n)
> @parallel for t in zip(x,y)
> x,y = t
> println(x,y)
> end
>
> exception on 1: ERROR: no method
> getindex(Zip2{Array{Int64,1},Array{Int64,1}}, Range1{Int64})
> in anonymous at no file:1467
> in anonymous at multi.jl:1278
> in run_work_thunk at multi.jl:575
> in run_work_thunk at multi.jl:584
> in anonymous at task.jl:88
>
