Slices and CuArrays about tensorcast.jl HOT 5 OPEN

Maybe @maleadt has some thoughts on how we can set up CI to track this.

from tensorcast.jl.

BTW, the sliced object here is an ordinary array of CuArrays. Which I think means that the iteration over slices happens on the CPU. I'm not sure precisely how that all works, but it sounds like it would work best for lots of work on few, large, slices.

julia> TensorCast.sliceview(cu(rand(2,3)), (:,*))
3-element Array{CuArray{Float32,1,CuArray{Float32,2,Nothing}},1}:
 Float32[0.61146635, 0.5048153]
 Float32[0.7750392, 0.87952733]
 Float32[0.7177145, 0.9034438] 

julia> TensorCast.red_glue(ans, (:,*)) # is just reduce(hcat, ans)
2×3 CuArray{Float32,2,Nothing}:
 0.611466  0.775039  0.717714
 0.504815  0.879527  0.903444

from tensorcast.jl.

GPU CI resources are documented here: https://github.com/JuliaGPU/gitlab-ci

from tensorcast.jl.

Thanks, that doesn't sound too difficult.

from tensorcast.jl.

This package still lacks its own GPU tests, sadly.

It could at least easily get fake GPU tests via JLArrays.

Besides slices above, it would be worth testing examples from #28 and #25.

from tensorcast.jl.