The one built-in to python would be multiprocessing docs are here. I always use multiprocessing.Pool with as many workers as processors. Then whenever I need to do a for-loop like structure I use Pool.imap
As long as the body of your function does not depend on any previous iteration then you should have near linear speed-up. This also requires that your inputs and outputs are pickle-able but this is pretty easy to ensure for standard types.
UPDATE:
Some code for your updated function just to show how easy it is:
from multiprocessing import Pool
from itertools import product
output = np.zeros((N,N))
pool = Pool() #defaults to number of available CPU's
chunksize = 20 #this may take some guessing ... take a look at the docs to decide
for ind, res in enumerate(pool.imap(Fun, product(xrange(N), xrange(N))), chunksize):
output.flat[ind] = res