[REFACTOR] cuda.parallel: Don't require passing input/output arrays to reduce_into and similar algorithms

[shwina]

Currently, reduce_into usage looks like:

# construct the reduer:
reducer = cudax.reduce_into(d_in, d_out, op, h_init)

# allocate temp storage
temp_storage_bytes = reducer.reduce_into(None, d_in, d_out, op, h_init)
d_temp = cuda.device_array(temp_storage_bytes)
result = reducer.reduce_into(d_temp, d_in, d_out, op, h_init)

Note that the initial construction of reducer shouldn't strictly need the arguments d_in, d_out, h_init. In fact, passing any placeholder arrays of the same data type will serve the same purpose.

We should refactor such that only the required information is passed into the constructor.

Additional Context

#3001

[leofang]

I was under the impression that d_in/d_out could be iterators too, in which case reduce_info would need to know them (as part of the problem definition) for later codegen?

[rwgk]

I was under the impression that d_in/d_out could be iterators too,

Yes. The API I have right now (in #2788) is hack-ish, just enough for full testing. I want to discuss with @shwina (today) what the iterator API should look like.