r = remote_call(2, rand)
fetch(r)
fetch(@spawnat 2 1+fetch(r)+myid())
shows transferring data to another process and then running a query on it
(iterate through the processes as per Strickland to do this everywhere)
I didn't find a way to do this with pmap - you can transfer the data each
time though
pmap(x->myid()+x[1]+x[2], map(x->(x,a),1:3))
On Monday, September 15, 2014 5:09:18 PM UTC+1, John Drummond wrote:
>
> @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?
>>>>>
>>>>
