Trying to understand what LoopVectorization does/doesn’t like.If I broadcast-add

General Usage
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Trying to understand what LoopVectorization does/doesn’t like.

If I broadcast-add elements of tuples

function bcast_add(x::NTuple{N,T},y::NTuple{N,T}) where {N,T}
    @avx for i = 1:length(first(x))
        x_i = getindex.(x,i) .+ getindex.(y,i)
        setindex!.(x,x_i,i)
    end
end

using @avx gives an Expression not recognized error which disappears if I remove getindex.(...) or setindex.(...). Are these operations not supported?

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from @Mason on Slack:

That’s correct, @avx doesn’t know how to deal with broadcasting, though this would be a conceivable feature to add. Perhaps open an issue in LoopVectorization.jl?

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More in-detail response to a related Github issue Broadcasted indexing · Issue #197 · JuliaSIMD/LoopVectorization.jl · GitHub

Solution using code generation

using LoopVectorization, Base.Cartesian
@generated function foo!(x::Tuple{Vararg{Array,N}}) where {N}
    quote
        @nextract $N x x
        @avx for i = 1:length(first(x))
            @nexprs $N n -> x_n[i] = exp(x_n[i])
        end
    end
end
using Random
x = (rand(100), randn(100), randexp(100))
foo!(x)
x
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Helpful comments from @mcabbott on Slack:

I could be wrong but don’t think any kind of two-deep indexing is going to work […] I think things like x[n][i] also won’t work, it needs to know what arrays it’s dealing with. You could generate code though, for each N build the unrolled expression with all N loops visible. Either just a loop for N in 1:10 @eval fun!(x::NTuple{$N,T}, y::...) or a generated function.

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