How to run functions in parallel?

If your functions are mainly doing I/O work (and less CPU work) and you have Python 3.2+, you can use a ThreadPoolExecutor:

from concurrent.futures import ThreadPoolExecutor

def run_io_tasks_in_parallel(tasks):
    with ThreadPoolExecutor() as executor:
        running_tasks = [executor.submit(task) for task in tasks]
        for running_task in running_tasks:
            running_task.result()

run_io_tasks_in_parallel([
    lambda: print('IO task 1 running!'),
    lambda: print('IO task 2 running!'),
])

If your functions are mainly doing CPU work (and less I/O work) and you have Python 2.6+, you can use the multiprocessing module:

from multiprocessing import Process

def run_cpu_tasks_in_parallel(tasks):
    running_tasks = [Process(target=task) for task in tasks]
    for running_task in running_tasks:
        running_task.start()
    for running_task in running_tasks:
        running_task.join()

run_cpu_tasks_in_parallel([
    lambda: print('CPU task 1 running!'),
    lambda: print('CPU task 2 running!'),
])

This can be done elegantly with Ray, a system that allows you to easily parallelize and distribute your Python code.

To parallelize your example, you'd need to define your functions with the @ray.remote decorator, and then invoke them with .remote.

import ray

ray.init()

dir1 = 'C:\\folder1'
dir2 = 'C:\\folder2'
filename = 'test.txt'
addFiles = [25, 5, 15, 35, 45, 25, 5, 15, 35, 45]

# Define the functions. 
# You need to pass every global variable used by the function as an argument.
# This is needed because each remote function runs in a different process,
# and thus it does not have access to the global variables defined in 
# the current process.
@ray.remote
def func1(filename, addFiles, dir):
    # func1() code here...

@ray.remote
def func2(filename, addFiles, dir):
    # func2() code here...

# Start two tasks in the background and wait for them to finish.
ray.get([func1.remote(filename, addFiles, dir1), func2.remote(filename, addFiles, dir2)]) 

If you pass the same argument to both functions and the argument is large, a more efficient way to do this is using ray.put(). This avoids the large argument to be serialized twice and to create two memory copies of it:

largeData_id = ray.put(largeData)

ray.get([func1(largeData_id), func2(largeData_id)])

Important - If func1() and func2() return results, you need to rewrite the code as follows:

ret_id1 = func1.remote(filename, addFiles, dir1)
ret_id2 = func2.remote(filename, addFiles, dir2)
ret1, ret2 = ray.get([ret_id1, ret_id2])

There are a number of advantages of using Ray over the multiprocessing module. In particular, the same code will run on a single machine as well as on a cluster of machines. For more advantages of Ray see this related post.

You could use threading or multiprocessing.

Due to peculiarities of CPython, threading is unlikely to achieve true parallelism. For this reason, multiprocessing is generally a better bet.

Here is a complete example:

from multiprocessing import Process

def func1():
  print 'func1: starting'
  for i in xrange(10000000): pass
  print 'func1: finishing'

def func2():
  print 'func2: starting'
  for i in xrange(10000000): pass
  print 'func2: finishing'

if __name__ == '__main__':
  p1 = Process(target=func1)
  p1.start()
  p2 = Process(target=func2)
  p2.start()
  p1.join()
  p2.join()

The mechanics of starting/joining child processes can easily be encapsulated into a function along the lines of your runBothFunc:

def runInParallel(*fns):
  proc = []
  for fn in fns:
    p = Process(target=fn)
    p.start()
    proc.append(p)
  for p in proc:
    p.join()

runInParallel(func1, func2)