On Monday, July 20, 2015 08:19:43 PM John Brock wrote:
> Isn't that what DArrays are for, though?
With a DArray, each chunk is local to only one process. That makes it really
expensive to read data from other chunks. With SharedArrays, you have fast
read access to the whole thing. But you have t
Isn't that what DArrays are for, though? Does Julia provide a mechanism for
mutual exclusion/marking critical sections? I'm imagining something like:
shared_result = SharedArray(Int64, (2,), init = S -> S[localindexes(S)] = 0
)
@parallel for i=1:3
lock shared_result
shared_result[:] +
Usually the whole point of a SharedArray is that workers only update the piece
they "own." You can make it work different if you implement locking, but lock
contention can be a bottleneck.
--Tim
On Monday, July 20, 2015 04:29:04 PM John Brock wrote:
> I'm seeing inconsistent results when multip
I'm seeing inconsistent results when multiple workers write values to a
SharedArray at the same time, presumably because += isn't atomic. Is this
intended behavior, and is there a workaround? Behavior is reproducible in
0.3.8-pre+22 and 0.3.9.
Sample code:
function doStuff()
result_shared =
