# Is this function well optimized for speed?

**URL:** <https://discourse.julialang.org/t/is-this-function-well-optimized-for-speed/31032>\
**Category:** General Usage\
**Created:** [November 13, 2019, 8:37am UTC](https://discourse.julialang.org/t/is-this-function-well-optimized-for-speed/31032 "2019-11-13T08:37:46Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![Seif\_Shebl](https://avatars.discourse-cdn.com/v4/letter/s/eada6e/32.png) [@Seif\_Shebl](https://discourse.julialang.org/u/Seif_Shebl)\
**Post date:** [November 13, 2019, 10:08pm UTC](https://discourse.julialang.org/t/is-this-function-well-optimized-for-speed/31032/11 "2019-11-13T22:08:00Z")

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You can achieve near 15% speedup without using any packages if you feed the function `Log_1_plus_Exp(x)` as a first argument to `sum`, it also cuts the total allocated memory in half. Using `xt = transpose(x)` is just for clarity, remember that `transpose` is lazy. BTW, [VML.jl](https://github.com/Crown421/VML.jl) isn’t supported in Windows yet.

```julia
function Log_Unnorm_Probs_B(x, W, Nh)
    Log_1_plus_Exp(x) = x > 50 ? x : log(1+exp(x))
    b = view(W,:,1)
    ww = view(W,:,2:Nh+1)
    xt = transpose(x)
    xt*b .+ sum(Log_1_plus_Exp, xt*ww, dims=2)
end

NNv = 300
NNh = 600
Nsamples = 4096
WW = randn(NNv+1,NNh+1)
xxx = rand([0.0,1.0], NNv+1, Nsamples)
xxx[1,:] .= 1.0

using BenchmarkTools
@btime Log_Unnorm_Probs_B(xxx, WW, NNh)
    71.201 ms (16 allocations: 18.84 MiB)

```

**Edit:**

Thanks to @Amin_Yahyaabadi, [Crown421/VML.jl](https://github.com/Crown421/VML.jl) now works nice in Windows.

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