Drop of performances with Julia 1.6.0 for InterpolationKernels

General Usage Performance
12

I’ve tracked it down. Indeed, I’m seeing a 3x slowdown for this “trivialized” loop function.

function increment!(p::AbstractArray,a::AbstractArray)
    for I ∈ CartesianIndices(p)
        @inbounds p[I] += a[I]
    end
end
n = 32
@benchmark increment!(a,b) setup=(a=rand(n,n,n);b=rand(n,n,n))

This gives a 3x slow down from Julia 1.5 to 1.6. The amount of slowdown does depend on n: The slowdown is only 1.7x when n=64 and is 4.3x when n=16. Using 2D arrays with the same total memory shows a similar slow down, but using 1D arrays makes this slowdown vanish.

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