# How to use multiple threads (julia \> 1.3) in a for loop saving results in a vector

**URL:** <https://discourse.julialang.org/t/how-to-use-multiple-threads-julia-1-3-in-a-for-loop-saving-results-in-a-vector/40611>\
**Category:** New to Julia\
**Tags:** multithreading\
**Created:** [June 2, 2020, 12:50pm UTC](https://discourse.julialang.org/t/how-to-use-multiple-threads-julia-1-3-in-a-for-loop-saving-results-in-a-vector/40611 "2020-06-02T12:50:28Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [June 2, 2020, 12:50pm UTC](https://discourse.julialang.org/t/how-to-use-multiple-threads-julia-1-3-in-a-for-loop-saving-results-in-a-vector/40611/1 "2020-06-02T12:50:28Z")

</div>

As I needed quite a few time to learn how to use the new @spawn in Julia \>= 1.3, here is an example on how to use threads on a function that produces something, saving the results to a vector (where order doesn’t matter):

```julia
import Base.Threads.@spawn

struct myobj
    o
end

singleOp(obj,x,y) = (x .+ y) .* obj.o

function multipleOps(obj,xbatch,ybatch)
    #out = Array{Float64,1}[]
    out = Array{Array{Float64,1},1}(undef,size(xbatch,1))
    for i in 1:size(xbatch,1)
        #println(i)
        #push!(out,singleOp(obj,xbatch[i,:],ybatch[i,:]))
        out[i] = singleOp(obj,xbatch[i,:],ybatch[i,:])
    end
    return out
end

obj = myobj(2)
xbatch = [1 2 3; 4 5 6]
ybatch = [10 20 30; 40 50 60]

results = @spawn multipleOps(obj,xbatch,ybatch)
finalres = sum(fetch(results))

```

However the advantage in terms of time became interesting only for relatively computationally expensive operations:

```julia
using BenchmarkTools

xbatch = rand(32,50)
ybatch = rand(32,50)
@benchmark sum(fetch(@spawn multipleOps(obj,xbatch,ybatch))) #60 μs
@benchmark sum(multipleOps(obj,xbatch,ybatch)) #24 μs

xbatch = rand(32,50000)
ybatch = rand(32,50000)
@benchmark sum(fetch(@spawn multipleOps(obj,xbatch,ybatch))) # 58 ms
@benchmark sum(multipleOps(obj,xbatch,ybatch)) # 66 ms

```

(by the way I couldn’t find a way to use a preallocated array instead of pushing into it)