@spawnat(2,whos()) is useful to know what's defined in other processes
(e.g. for process 2 in this case)
if you want to gather the return values, use a reducer function
e.g.
let a = rand()
@parallel hcat for x in 1:3
foo(a,x)
end
end
trying to remember the other approach which I thought I got working rather
than copying it over each run.
One other thing is that requiring a file after adding the processes should
load it on all processes which is another approach to setting up the
environment for each process
On Monday, September 15, 2014 3:51:43 PM UTC+1, John Drummond wrote:
>
> with parallel
>
> addprocs(2)
> println("Number of processors", nprocs())
> @everywhere function foo(x,y)
> println(myid()," ", x, " ", y)
> return x + y
> end
> let a = rand()
> @sync @parallel for x in 1:3
> foo(a,x)
> end
> end
>
> On Monday, September 15, 2014 3:14:50 PM UTC+1, [email protected] wrote:
>>
>> "you could precreate A and then define it everywhere"
>> Could you please show me some code how to do it?
>>
>> On Monday, September 15, 2014 2:51:08 PM UTC+2, John Drummond wrote:
>>>
>>> Chris Strickland
>>> <https://groups.google.com/forum/#!msg/julia-users/jlKoEtErRL4/0ZcB_hxyJlYJ>
>>>
>>> lists one approach for the general problem
>>>
>>> you could precreate A and then define it everywhere, or send a copy over
>>> as a parameter to whatever function you use in pmap, similar but not the
>>> same as above
>>>
>>> Another approach which was useful to me was the @parallel for loops
>>> "Any variables used inside the parallel loop will be copied and
>>> broadcast to each process."
>>> <http://julia.readthedocs.org/en/latest/manual/parallel-computing/>
>>>
>>> and shared arrays
>>> <http://julia.readthedocs.org/en/latest/manual/parallel-computing/#shared-arrays-experimental-unix-only-feature>
>>>
>>> if using linux could be useful (I've not tried them).
>>>
>>>
>>>
>>> On Monday, September 15, 2014 10:52:33 AM UTC+1, [email protected]
>>> wrote:
>>>>
>>>> I want to transfer a variable to all parallel workers. However, if I do:
>>>>
>>>> A=rand()
>>>> pmap(x->A+x,1:3)
>>>>
>>>> Return error:
>>>> exception on 2: ERROR: A not defined
>>>> in anonymous at none:1
>>>> in anonymous at multi.jl:855
>>>> in run_work_thunk at multi.jl:621
>>>> in anonymous at task.jl:855
>>>> exception on 3: ERROR: A not defined
>>>> in anonymous at none:1
>>>> in anonymous at multi.jl:855
>>>> in run_work_thunk at multi.jl:621
>>>> in anonymous at task.jl:855
>>>> 2-element Array{Any,1}:
>>>> UndefVarError(:A)
>>>> UndefVarError(:A)
>>>>
>>>> The result of
>>>> @everywhere A=rand()
>>>> pmap(x->A+x,1:3)
>>>> is not what I want, since I hope A in all mashines are the same.
>>>>
>>>> I know that pmap((x,y)->x+y,1:3,fill(A,3)) will be work, but I don't
>>>> think it is smart since A is expand in memery unnessarily. Is there any
>>>> simple way to just send a copy of A, or the reference of A, to all
>>>> parallel
>>>> mashines?
>>>>
>>>
