[julia-users] Parallel computing and multiple outputs

[Archibald Pontier]

Hi everyone,

I have the following problem which originates from the fact that 
SharedArray doesn't seem to accept composite types as argument. Consider 
the following sequence (I started julia with -p 2):

julia> @everywhere type T
       t::Array{Float64, 1}
       end

julia> @everywhere type T2
       t::Array{Float64, 1}
       end

julia> @everywhere type T3
       t::Array{Float64, 1}
       end

julia> @everywhere function test(t::T, t2::T2, i)
       return T3([t.t[i], t2.t[i]]);
       end

julia> @everywhere t = T(rand(3))

julia> @everywhere t2 = T2(rand(3))

julia> t3 = pmap(i -> test(t, t2, i), 1:3)
3-element Array{Any,1}:
 T3([0.521706,0.0155359])
 T3([0.112277,0.59876])  
 T3([0.0399843,0.373688])

julia> @everywhere function test2(t3::T3, t::T, t2::T2)
       t.t[1] += t3.t[1];
       t2.t[2] += t3.t[2];
       return (t, t2);
       end

julia> A = pmap(i -> test2(t3[i], t, t2), 1:3)
exception on exception on 2: 3: ERROR: t3 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
ERROR: t3 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(:t3)
 UndefVarError(:t3)

The problem solves itself if I do @everywhere t3 = pmap(...), however from 
my understanding this would only lead to every operation done on every 
process, which defeats the purpose of doing things in parallel in the first 
place. Am I wrong with this conclusion?

Regards,
Archibald