Leverage linear indexing for pointwise kernels

[charleskawczynski]

We are currently suffering pretty badly from these two issues:

• Broadcasting is much slower than a for loop JuliaLang/julia#28126
• Broadcast and linear indexing JuliaLang/julia#32051

And this impact is notably important on the GPU, as Tim notes here.

Here's a benchmark showing the impact on a local benchmark with a target resolution on a single A100. All the kernel is doing is @. x = zero(x):

julia> @pretty_belapsed at_dot_call!($X_array, $Y_array)
     6 milliseconds, 847 microseconds
julia> @pretty_belapsed at_dot_call!($X_vector, $Y_vector)
     2 milliseconds, 901 microseconds

Since we have styles that basically categorize the type of broadcast expression we have, I think we can transform the broadcasted objects in our pointwise case, and force linear indexing there.

This could be a limitation for when we have views, but we can try it out since SubArrays seem to safely support linear indexing:

julia> a = rand(3,3,3);

julia> b = view(a, :,2,:);

julia> b[9]
0.7040413357481441

julia> b[10]
ERROR: BoundsError: attempt to access 3×3 view(::Array{Float64, 3}, :, 2, :) with eltype Float64 at index [10]
Stacktrace:
 [1] throw_boundserror(A::SubArray{Float64, 2, Array{Float64, 3}, Tuple{Base.Slice{…}, Int64, Base.Slice{…}}, false}, I::Tuple{Int64})
   @ Base ./abstractarray.jl:737
 [2] checkbounds
   @ ./abstractarray.jl:702 [inlined]
 [3] _getindex
   @ ./abstractarray.jl:1335 [inlined]
 [4] getindex(A::SubArray{Float64, 2, Array{Float64, 3}, Tuple{Base.Slice{…}, Int64, Base.Slice{…}}, false}, I::Int64)
   @ Base ./abstractarray.jl:1291
 [5] top-level scope
   @ REPL[11]:1
Some type information was truncated. Use `show(err)` to see complete types.

xref: #1403

[charleskawczynski]

This is only possible for datalayouts that start and end with the field index, so this was closed by #2055.