multiprocessing with large data

I am using multiprocessing.Pool() to parallelize some heavy computations.

The target function returns a lot of data (a huge list). I'm running out of RAM.

Without multiprocessing, I'd just change the target function into a generator, by yielding the resulting elements one after another, as they are computed.

I understand multiprocessing does not support generators -- it waits for the entire output and returns it at once, right? No yielding. Is there a way to make the Pool workers yield data as soon as they become available, without constructing the entire result array in RAM?

Simple example:

def target_fnc(arg):
   result = []
   for i in xrange(1000000):
       result.append('dvsdbdfbngd') # <== would like to just use yield!
   return result

def process_args(some_args):
    pool = Pool(16)
    for result in pool.imap_unordered(target_fnc, some_args):
        for element in result:
            yield element

This is Python 2.7.