The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I'll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool pool = ThreadPool(4) results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results =  for item in my_array: results.append(my_function(item))
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead ( an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
import urllib2 from multiprocessing.dummy import Pool as ThreadPool urls = [ 'http://www.python.org', 'http://www.python.org/about/', 'http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html', 'http://www.python.org/doc/', 'http://www.python.org/download/', 'http://www.python.org/getit/', 'http://www.python.org/community/', 'https://wiki.python.org/moin/', ] # Make the Pool of workers pool = ThreadPool(4) # Open the URLs in their own threads # and return the results results = pool.map(urllib2.urlopen, urls) # Close the pool and wait for the work to finish pool.close() pool.join()
And the timing results:
Single thread: 14.4 seconds 4 Pool: 3.1 seconds 8 Pool: 1.4 seconds 13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
Or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround).
(Thanks to user136036 for the helpful comment.)