reduce doesn't query src backend if backend specified

[dstahlke]

This allows calling reduce on MappedArrays types. That's nice because you can avoid allocating a large array to store the input. For example,

julia> using AcceleratedKernels, CUDA, MappedArrays
julia> AcceleratedKernels.sum(mappedarray(sin, 1:10_000_000_000), CUDABackend(false,false))

I'm creating a bare-bones pull request to get feedback on whether this is desired. If so, I can add a unit test.

[anicusan]

This is great, one of the primary reasons I added an explicit backend argument to all functions was to allow unmaterialised types such as ranges - e.g. having any work over eachindex(v) instead of v directly for more complex conditions. I haven't tested that on reduce, for which the temp check was failing.

A test for such unmaterialised types - MappedArrays or simple ranges - would be very useful; and perhaps including such tests for the other functions, thanks!

[dstahlke]

Unit tests added for range and mappedvector.

I tried to also get this working for mapreduce_nd. There are a variety of InvalidIRError, for example "unsupported dynamic function invocation (call to convert)" and "unsupported dynamic function invocation (call to print_to_string". Test case:

AK.mapreduce(x->x[1], +, CartesianIndices((12:345, 67:89)), CUDABackend(), init=10)