Thank you, it turns out my problem was coming from an @everywhere macro,
not from pmap.
However, and I hope it is not bad practice continuing in this same thread,
but now I'm seeing that pmap is not utilizing all of the workers available
for the process, in fact it is using only one, despite having 8 local and 8
remote workers available. What sort of problems could be causing this
behavior?
On Saturday, February 22, 2014 6:32:18 PM UTC-5, Stefan Karpinski wrote:
>
> If there are other processors, pmap doesn't use the head node by default:
>
> julia> addprocs(2)
> 2-element Array{Any,1}:
> 2
> 3
>
> julia> pmap(x->myid(), 1:10)
> 10-element Array{Any,1}:
> 2
> 3
> 3
> 2
> 2
> 3
> 2
> 3
> 2
> 3
>
>
> On Sat, Feb 22, 2014 at 5:50 PM, Micah McClimans
> <[email protected]<javascript:>
> > wrote:
>
>> I am working on distributing a compute intensive task over a cluster in
>> Julia, using the pmap function. However, for several reasons I would like
>> to avoid having the master node used in the computation- is there a way to
>> accomplish this using the built in keyword, or will I need to rewrite pmap?
>>
>
>
