Sure.
Invoked with julia -p 8
fid=open("/home/main/data/juliafiles/Julia/machinefile.txt")
rc=readlines(fid)
m={match(r"(\r|\n)",rcd) for rcd in rc}
machines={rc[ma][1:(m[ma].offset-1)] for ma in 1:length(m)}
...
addprocs(machines;dir="/home/main/data/programfiles/julia/usr/bin")
...
@everywhere progfile="/home/main/data/juliafiles/Julia/WLPPInt.jl"@everywhere
marketHistoryFile="/home/main/data/juliafiles/Julia/daily.mat"
@everywhere using MAT
@everywhere include(progfile)
@everywhere Daily= matread(marketHistoryFile)@everywhere marketHistory=
NYSE["NYSE_Smoothed_Closes"]
...
results=pmap(runWLPPIntTest,capitals,{marketHistory for i in
1:trials},depths,ALRs,{vec(variances) for i in 1:trials},{weights[:,i] for i in
1:trials},sigComponents)
I'm not really sure if this is enough to be useful though, or what really would
be able to be useful.
On Sunday, February 23, 2014 3:58:04 AM UTC-5, Amit Murthy wrote:
>
> Is it possible to share the relevant portions of the call here?
>
>
> On Sun, Feb 23, 2014 at 11:44 AM, Micah McClimans
> <[email protected]<javascript:>
> > wrote:
>
>> 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]>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?
>>>>
>>>
>>>
>
