I seems like what you need is the map method in multiprocessing.Pool():
map(func, iterable[, chunksize])
A parallel equivalent of the map() built-in function (it supports only one iterable argument though). It blocks till the result is ready. This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integ
For example, if you wanted to map this function:
def f(x):
return x**2
to range(10), you could do it using the built-in map() function:
map(f, range(10))
or using a multiprocessing.Pool() object’s method map():
import multiprocessing
pool = multiprocessing.Pool()
print pool.map(f, range(10))