# Performance and allocation issue with arrays of functions (v1.5)

**URL:** https://discourse.julialang.org/t/performance-and-allocation-issue-with-arrays-of-functions-v1-5/45013
**Category:** Performance
**Tags:** performance, memory-allocation
**Created:** [August 16, 2020, 12:14am UTC](https://discourse.julialang.org/t/performance-and-allocation-issue-with-arrays-of-functions-v1-5/45013 "2020-08-16T00:14:39Z")
**Posts on this page:** 1
**Showing post:** 7

<div class="post-metadata">

### Author: ![Elrod](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/elrod/32/22461_2.png) [@Elrod](https://discourse.julialang.org/u/Elrod)
#### Post date: [August 17, 2020, 2:44am UTC](https://discourse.julialang.org/t/performance-and-allocation-issue-with-arrays-of-functions-v1-5/45013/7 "2020-08-17T02:44:14Z")

</div>

Sure, but `@generated` is easy and it performs well. Comparing the above `compute_values2` with the generated version:

```julia
julia> @btime compute_values2($x₀, $Δt, $nΔt, $[f1])
  1.665 μs (1 allocation: 896 bytes)
 1×100 Matrix{Float64}:
 0.948985 0.999966 0.927798 0.64436 0.0897141 -0.623416 -0.998433 -0.317148 0.919731 … 0.265737 -0.944011 0.0275612 -0.999406 0.342549 0.939933 -0.676239 0.599191

julia> @btime compute_values2($x₀, $Δt, $nΔt, $[f2])
  231.457 ns (1 allocation: 896 bytes)
 1×100 Matrix{Float64}:
 1.5625 2.44141 3.8147 5.96046 9.31323 14.5519 22.7374 35.5271 55.5112 86.7362 135.525 … 1.65608e18 2.58763e18 4.04317e18 6.31746e18 9.87103e18 1.54235e19 2.40992e19

julia> @btime compute_values2($x₀, $Δt, $nΔt, $(f1,f2))
  1.726 μs (1 allocation: 1.77 KiB)
 2×100 Matrix{Float64}:
 0.948985 0.999966 0.927798 0.64436 0.0897141 -0.623416 -0.998433 -0.317148 0.919731 … -0.944011 0.0275612 -0.999406 0.342549 0.939933 -0.676239 0.599191
 1.5625 2.44141 3.8147 5.96046 9.31323 14.5519 22.7374 35.5271 55.5112 1.65608e18 2.58763e18 4.04317e18 6.31746e18 9.87103e18 1.54235e19 2.40992e19

julia> @btime compute_values_generated($x₀, $Δt, $nΔt, $(f1,))
  1.506 μs (1 allocation: 896 bytes)
 1×100 Matrix{Float64}:
 0.948985 0.999966 0.927798 0.64436 0.0897141 -0.623416 -0.998433 -0.317148 0.919731 … 0.265737 -0.944011 0.0275612 -0.999406 0.342549 0.939933 -0.676239 0.599191

julia> @btime compute_values_generated($x₀, $Δt, $nΔt, $(f2,))
  220.222 ns (1 allocation: 896 bytes)
 1×100 Matrix{Float64}:
 1.5625 2.44141 3.8147 5.96046 9.31323 14.5519 22.7374 35.5271 55.5112 86.7362 135.525 … 1.65608e18 2.58763e18 4.04317e18 6.31746e18 9.87103e18 1.54235e19 2.40992e19

julia> @btime compute_values_generated($x₀, $Δt, $nΔt, $(f1,f2))
  1.550 μs (1 allocation: 1.77 KiB)
 2×100 Matrix{Float64}:
 0.948985 0.999966 0.927798 0.64436 0.0897141 -0.623416 -0.998433 -0.317148 0.919731 … -0.944011 0.0275612 -0.999406 0.342549 0.939933 -0.676239 0.599191
 1.5625 2.44141 3.8147 5.96046 9.31323 14.5519 22.7374 35.5271 55.5112 1.65608e18 2.58763e18 4.04317e18 6.31746e18 9.87103e18 1.54235e19 2.40992e19

```

Although if you’re unhappy with laziness as an excuse, with a little more work, we can use dispatch instead of `Base.Cartesian.@nexprs` to unroll our expressions:

```julia
@inline fmap(fs::Tuple, x) = (first(fs)(x), fmap(Base.tail(fs), x)...)
@inline fmap(fs::Tuple{T}, x) where {T} = (first(fs)(x), )
function compute_values_map(x₀, Δt, nΔt, f::Tuple{Vararg{<:Any,K}}) where {K}
    x = x₀;
    f_vals = zeros(K, nΔt)
    for n in 1:nΔt
        x += 0.5 * Δt * x
        fv = fmap(f, x)
        for k in 1:K
            f_vals[k,n] = fv[k]
        end
    end
    return f_vals
end

```

Result:

```julia
julia> @btime compute_values_map($x₀, $Δt, $nΔt, $(f1,f2))
  1.562 μs (1 allocation: 1.77 KiB)
 2×100 Matrix{Float64}:
 0.948985 0.999966 0.927798 0.64436 0.0897141 -0.623416 -0.998433 -0.317148 0.919731 … -0.944011 0.0275612 -0.999406 0.342549 0.939933 -0.676239 0.599191
 1.5625 2.44141 3.8147 5.96046 9.31323 14.5519 22.7374 35.5271 55.5112 1.65608e18 2.58763e18 4.04317e18 6.31746e18 9.87103e18 1.54235e19 2.40992e19

```

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