# Correct way to populate an empty array in parallel in Julia?

**URL:** <https://discourse.julialang.org/t/correct-way-to-populate-an-empty-array-in-parallel-in-julia/31187>\
**Category:** General Usage\
**Created:** [November 17, 2019, 5:34pm UTC](https://discourse.julialang.org/t/correct-way-to-populate-an-empty-array-in-parallel-in-julia/31187 "2019-11-17T17:34:02Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![bashonubuntu](https://avatars.discourse-cdn.com/v4/letter/b/f19dbf/32.png) [@bashonubuntu](https://discourse.julialang.org/u/bashonubuntu)\
**Post date:** [November 17, 2019, 5:34pm UTC](https://discourse.julialang.org/t/correct-way-to-populate-an-empty-array-in-parallel-in-julia/31187/1 "2019-11-17T17:34:02Z")

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Hello,

Often times in parallel code, I find myself in need of populating an empty array. I have gone back and forth between using a `vcat` operation versus a `SharedArray`. A typical pseudo-code would look like:

```julia
function my_parallel_sharedarray()

 LL = SharedArray{Float64}(10, 1)
  @distributed for i in 1:10 
    LL[i] = #value
  end
  return sum(LL)
end

function my_parallel_vcat()

  z = @distributed (vcat) for i in 1:10 
    LL = #value
  end
 return sum(z)
end

```

What would be a preferred way to allocate an empty array (entry-by-entry, row-by-row or column-by-column) in parallel especially when the final array to be allocated in large (~1000 x 1 or 1000 \* 100 etc.)? If one of the above ways is strictly preferred, could someone please point me to the downside of the other sub-optimal way, ideally with the help of a small toy example?

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<div class="post-metadata">

**Author:** ![Mason](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mason/32/2423_2.png) [@Mason](https://discourse.julialang.org/u/Mason)\
**Post date:** [November 17, 2019, 10:01pm UTC](https://discourse.julialang.org/t/correct-way-to-populate-an-empty-array-in-parallel-in-julia/31187/2 "2019-11-17T22:01:19Z")

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Is it required that you start with an empty array? If not, I suggest instead doing something like

```julia
using Distributed: pmap, addprocs

addprocs(5)

pmap([(i, j) for i in 1:10, j in 1:10]) do (i, j)
    (factorial(i) + factorial(j))/(factorial(i)*factorial(j))
end

```

which will create and fill the following matrix in parallel:

```julia
10×10 Array{Float64,2}:
 2.0 1.5 1.16667 1.04167 … 1.0 1.0        
 1.5 1.0 0.666667 0.541667 0.500003 0.5        
 1.16667 0.666667 0.333333 0.208333 0.166669 0.166667   
 1.04167 0.541667 0.208333 0.0833333 0.0416694 0.0416669  
 1.00833 0.508333 0.175 0.05 0.00833609 0.00833361 
 1.00139 0.501389 0.168056 0.0430556 … 0.00139164 0.00138916 
 1.0002 0.500198 0.166865 0.0418651 0.000201168 0.000198688
 1.00002 0.500025 0.166691 0.0416915 2.75573e-5 2.50772e-5 
 1.0 0.500003 0.166669 0.0416694 5.51146e-6 3.03131e-6 
 1.0 0.5 0.166667 0.0416669 3.03131e-6 5.51146e-7 

```

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<div class="post-metadata">

**Author:** ![bashonubuntu](https://avatars.discourse-cdn.com/v4/letter/b/f19dbf/32.png) [@bashonubuntu](https://discourse.julialang.org/u/bashonubuntu)\
**Post date:** [November 17, 2019, 10:19pm UTC](https://discourse.julialang.org/t/correct-way-to-populate-an-empty-array-in-parallel-in-julia/31187/3 "2019-11-17T22:19:34Z")

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Thanks for the very helpful suggestion! Yes, I would prefer to do it by first pre-allocating an empty array. The reason is that each value is tied to a unique id and if there are issues in a value (say, I get something like a NaN, -Inf or Inf), I can look at the id corresponding to that value and then look back at all the data linked to that id. It would be simpler to examine issues if I can easily track ids from values and the pre-allocated array is a very simple way to achieve that. My worry is that if `vcat` keeps creating a copy then the `vcat` approach will allocate unnecessarily for large matrices but I want to better understand what `vcat` would do relative to a pre-allocated `SharedArray` in parallel mode drawing on the experience of julians here!