Julia: Building multiple types in parallel - julia

Question

I am trying to use parallelism in Julia to construct several large types (specifically, gaussian mixture models via sklearn through PyCall).
If I were doing this in series, I would do:
models = Array(GMM, N)
for i = 1 : N
params = ...
models[i] = train_gmm(params)
end
However, I should be able to do this is parallel. I am having trouble figuring out where to start, since SharedArrays and #parallel don't seem to be the right thing for me.
I was trying to use #spawn, but found the following:
function f1()
rand(10000000)
rand(10000000)
rand(10000000)
rand(10000000)
rand(10000000)
end
function f2()
a = #spawn rand(10000000)
b = #spawn rand(10000000)
c = #spawn rand(10000000)
d = #spawn rand(10000000)
e = #spawn rand(10000000)
a_r = fetch(a)
b_r = fetch(b)
c_r = fetch(c)
d_r = fetch(d)
e_r = fetch(e)
end
f1()
f2()
println(#elapsed(f1()))
println(#elapsed(f2()))
f1 takes 0.21 seconds, f2 takes 0.32 seconds! Is there something about #spawn that I am missing?
EDIT
It looks like doing:
function f1()
[sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000))]
end
function f2()
a = #spawn sum(rand(100000000))
b = #spawn sum(rand(100000000))
c = #spawn sum(rand(100000000))
d = #spawn sum(rand(100000000))
e = #spawn sum(rand(100000000))
[fetch(a), fetch(b), fetch(c), fetch(d), fetch(e)]
end
Causes f2() to run faster than f1() and it is more in-line with what I want. I'll go with this unless someone has a better, official way.
Thank you.

Answer 1

I think your edit has got it correct.
IAINMAC:~ idunning$ julia -p 3
julia> #everywhere function foo()
sleep(2)
end
julia> #time [foo(), foo(), foo()]
elapsed time: 6.017959282 seconds (294088 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing
julia> function bar()
a = #spawn foo()
b = #spawn foo()
c = #spawn foo()
[fetch(a), fetch(b), fetch(c)]
end
bar (generic function with 1 method)
julia> #time bar()
elapsed time: 2.030760103 seconds (199720 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing
or even more elegantly with pmap:
julia> #everywhere function foo(a::Int)
sleep(a)
end
julia> #time pmap(foo,1:3)
elapsed time: 3.004821524 seconds (448540 bytes allocated)
3-element Array{Any,1}:
nothing
nothing
nothing
julia> #time map(foo,1:3)
elapsed time: 6.006557822 seconds (1368 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing