[FEA]: Identify gaps in tensor algorithms coverage

### Is this a duplicate?

- [x] I confirmed there appear to be no [duplicate issues](https://github.com/NVIDIA/cccl/issues) for this request and that I agree to the [Code of Conduct](CODE_OF_CONDUCT.md)

### Area

CUB

### Is your feature request related to a problem? Please describe.

As of https://github.com/NVIDIA/cccl/issues/7277 @fbusato added tensor copy algorithm. We occasionally get requests for other classes of tensor algorithms, such as reductions and cumulative sums along a dimension. Before opening implementation issues, we should identify which algorithms need `mdspan` overloads or tensor-aware APIs.

### Describe the solution you'd like

As a first step, we should review CuPy (cc @leofang) and similar array libraries to see which generic kernels they had to reimplement to support multidimensional, strided, broadcasted, or axis-based input. This should help us distinguish cases covered by existing primitives from cases where users still need custom tensor kernels.

This issue can be closed with a comment summarizing the findings of
1) Should CCCL add more mdspan-based algorithms?
2.) If so, file issues describing which ones

### Describe alternatives you've considered

_No response_

### Additional context

_No response_