[EPIC] Investigate refactoring CuPy to use cuda.parallel

### 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

cuda.parallel (Python)

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

Today, CuPy uses Thrust/CUB algorithms to implement much of it's functionality. That works today by precompiling Thrust algorithms for a variety of fixed types. This is undesirable for a few reasons: it increases binary size, it limits exposure of some algorithms (like segmented sort) due to combinatorial type explosion.

`cuda.parallel` can and should be able to replace any existing use of pre-instantiated Thrust/CUB algorithms and provide a few benefits:
- Reduce binary size (going to JIT)
- Custom type support
- Custom operator support
- Additional algorithm support (because JIT avoids the type combination problem)

### Describe the solution you'd like

To start, we'd like to have an inventory of what Thrust/CUB stuff CuPy is using today and where.

From there, we should investigate how we can use `cuda.parallel.reduce_into` to replace existing uses of cub::DeviceReduce/thrust::reduce.

### Describe alternatives you've considered

_No response_

### Additional context

_No response_