Mapslices and @threads

General Usage
#1

I am fooling around with 1.30-rc1 and would like to understand how to make a call on mapslices threaded.

Since mapslices can emulate a loop over an array dimension, it would nice to make that “loop” Threads.@threads. Does anyone have a hint?

#2

Writing a loop over eachcol(x) is usually faster than mapslices even before you add threading. Or you can use my SliceMap, which has tmapcols. Or use JuliennedArrays for more complicated slicing.

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#3

My use case us that I want to write code that can take an AbstractArray as input and then do operations along one dimension. In some calls the array will be a Matrix, but in other cases there will be more dimensions.

This rules out eachcol and probably SliceMap, or? Not even eachslice seems to work well since it only allows a single dimension in dims.

#4

If I understand right, then yes, eachslice allows only one “outer” dimension, meaning that your function will get N-1-dimensional arrays when acting on an N-array. Slightly confusingly mapslices takes the “inner” dimensions as dims instead, as do Slices and slicemap. But these should be more general:

julia> map(ndims, eachslice(ones(1,2,3), dims=3))
3-element Array{Int64,1}:
 2
 2
 2

julia> mapslices(ndims, ones(1,2,3), dims=(1,2))
1×1×3 Array{Int64,3}:
[:, :, 1] =
 2

[:, :, 2] =
 2

[:, :, 3] =
 2

julia> using JuliennedArrays

julia> map(ndims, Slices(ones(1,2,3), 1,2))
3-element Array{Int64,1}:
 2
 2
 2

julia> using SliceMap

julia> slicemap(ndims, ones(1,2,3), dims=(1,2))
3-element Array{Int64,1}:
 2
 2
 2

Here of course the function gives a scalar; had it made an array then the map examples would be arrays of arrays, needing reduce(hcat,... or Align etc, which is built into mapslices / slicemap.

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