# Help improve my threaded map function?

**URL:** https://discourse.julialang.org/t/help-improve-my-threaded-map-function/4112
**Category:** General Usage
**Created:** [June 6, 2017, 3:21pm UTC](https://discourse.julialang.org/t/help-improve-my-threaded-map-function/4112 "2017-06-06T15:21:02Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![marius311](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/marius311/32/3953_2.png) [@marius311](https://discourse.julialang.org/u/marius311)
#### Post date: [June 6, 2017, 3:21pm UTC](https://discourse.julialang.org/t/help-improve-my-threaded-map-function/4112/1 "2017-06-06T15:21:02Z")

</div>

I tried writing a @thread’ed map function for array arguments. Very roughly imitating the general idea for how I see Base.map handles inferring the type of the result, I came up with,

```julia
function tmap(f,args::AbstractArray...)
    cargs = collect(zip(args...))
    n = length(cargs)
    T = Core.Inference.return_type(f, Tuple{map(typeof,cargs[1])...})
    ans = Vector{T}(n)
    @threads for i=1:n
        ans[i] = f(cargs[i]...)
    end
    ans
end

```

It works, but I was surprised to see that if I replace the type inference line above with just `T=Any`, I get a ~20% speed improvement. Here’s what I benchmarked, just multiplying a bunch of matrices together,

```julia
m = [randn(128,128) for i=1:10]
@benchmark tmap(*,$m,$m)

```

I get 100ms for the inferred version, but 80ms without it.

The overhead of the call to Inference.return\_type appears negligible. Any ideas what’s going on?