I usually use a combination of @spawn and fetch. @spawn fires a lightweigth
task that can return a value of any type, which can be collected by the
main thread with fetch:
refs = Vector{RemoteRef}(N)
for i in 1:N
refs[i] = @spawn begin doSomethingAndReturnValueOfTypeT end
end
for i in 1:N
v[i] = fetch(refs[i])
end
I'm not aware if there is a better way, as far as I know SharedArrays don't
work with user-defined types.
You need to have worker threads for this to be of any use, by using
addprocs() or by starting julia with the -p flag, for instance $ julia -p 4.
I hope that helps,
Germán.
On Friday, September 9, 2016 at 7:51:59 AM UTC-3, ami...@gmail.com wrote:
>
> Hello,
>
> What would be the best option to parallelize this code:
>
> type T a end
> f(i) = T(i)
> v = map(f, collect(1:1:100))
>
> This example could sound stupid but the point is that I have a function
> ```f``` that returns a rather complicated user-defined type ```T```, and I
> need to store a lot of these types in an ```Array```.
> I've read a bit about the parallel paradigm of Julia and I honestly
> wouldn't know how to do such a thing.
>
> Any hint?
>
> Many thanks!
>
