Can this be written even faster? (CPU)

General Usage Performance
18

Okay I found the issue! It is something with the inverse of gamma in the power calculation. If I write it directly no issue anymore on Julia 1.10:

using BenchmarkTools
using StaticArrays
using LoopVectorization

# Generate the needed constants
N     = 6000
Cb    = inv(Float64(100000))
xᵢⱼᶻ  = rand(Float64,N*4)
xᵢⱼ   = rand(SVector{3,Float64},N*4)
ρ₀    = Float64(1000)
g     = Float64(9.81)
drhopLp = zeros(Float64, length(xᵢⱼ))
drhopLn = zeros(Float64, length(xᵢⱼ))

function loopvectorization_approach(Cb,xᵢⱼᶻ,ρ₀,γ⁻¹,g,drhopLp,drhopLn)
    @tturbo for iter in eachindex(xᵢⱼᶻ)
        # IF I COMMENT '* -xᵢⱼᶻ[iter]' NO ALLOCATIONS, WHY?
        Pᵢⱼᴴ  = ρ₀ * (-g) * -xᵢⱼᶻ[iter]
        ρᵢⱼᴴ  = ρ₀ * ( ^( 1 + (Pᵢⱼᴴ * Cb), 0.14285714285714285) - 1)  
        Pⱼᵢᴴ  = -Pᵢⱼᴴ
        ρⱼᵢᴴ  = ρ₀ * ( ^( 1 + (Pⱼᵢᴴ * Cb), 0.14285714285714285) - 1)
        
        drhopLp[iter] = ρᵢⱼᴴ
        drhopLn[iter] = ρⱼᵢᴴ
    end
end

l  = @benchmark loopvectorization_approach($Cb,$xᵢⱼᶻ,$ρ₀,$γ⁻¹,$g,$drhopLp,$drhopLn)

display(l)

julia> l  = @benchmark loopvectorization_approach($Cb,$xᵢⱼᶻ,$ρ₀,$γ⁻¹,$g,$drhopLp,$drhopLn)
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  339.900 μs …  4.994 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     358.600 μs              ┊ GC (median):    0.00%
 Time  (mean ± σ):   382.900 μs ± 97.110 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

   ▅█▇▅▄▃▂▂▁▁▁▁▁          ▁▁▁▁                                 ▂
  █████████████████▇▇█▇█████████▇█▇▇▇▇▇▆▄▆▅▆▆▆▅▅▆▄▆▅▁▅▅▅▄▄▆▆▇▇ █
  340 μs        Histogram: log(frequency) by time       718 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

Now on to see if I can trick it and use the variable.

EDIT: Both ways are Float64 values, I am confused :sweat_smile:

Kind regards

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