Using Python's Multiprocessing Pool for Functions with Multiple Arguments

Introduction

The Python multiprocessing library is a powerful tool that allows you to perform parallel processing by leveraging multiple CPU cores. One common use case involves applying a function to each element of an iterable using the Pool.map() method. However, when your function requires multiple arguments, the built-in map() method can be limiting because it only supports single-argument functions.

In this tutorial, we will explore how to use Python’s multiprocessing Pool class to apply functions that require multiple arguments. We’ll cover various approaches for different versions of Python, including the use of pool.starmap(), functools.partial, and helper functions.

Understanding the Basics

The primary goal is to parallelize a function call over an iterable using multiple arguments. For instance, if you have a function that takes two parameters and you need to apply this function across multiple pairs of data, traditional map() won’t suffice because it can only pass one argument at a time.

Example Function

Consider the following simple example where we want to merge names:

def merge_names(a, b):
    return f'{a} & {b}'

Using pool.starmap()

Python 3.3 introduced starmap() in the multiprocessing module, which is designed for this very purpose. The starmap() method allows you to pass each tuple of arguments from an iterable to your function.

Example

import multiprocessing
from itertools import product

def merge_names(a, b):
    return f'{a} & {b}'

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with multiprocessing.Pool(processes=3) as pool:
        results = pool.starmap(merge_names, product(names, repeat=2))
    print(results)

In this example, product() generates all possible pairs of names, and each pair is passed to merge_names().

Handling Multiple Arguments in Older Python Versions

For older versions of Python (before 3.3), you will need to implement a workaround using helper functions or context managers.

Using Helper Functions

One approach involves creating an unpacking function that takes a tuple as its argument and unpacks it within the function call:

import multiprocessing
from itertools import product

def merge_names(a, b):
    return f'{a} & {b}'

def merge_names_unpack(args):
    return merge_names(*args)

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with multiprocessing.Pool(processes=3) as pool:
        results = pool.map(merge_names_unpack, product(names, repeat=2))
    print(results)

Using a Context Manager for Pool

To manage the lifecycle of a Pool object more cleanly in older versions:

import multiprocessing
from itertools import product
from contextlib import contextmanager

@contextmanager
def poolcontext(*args, **kwargs):
    pool = multiprocessing.Pool(*args, **kwargs)
    yield pool
    pool.terminate()

def merge_names(a, b):
    return f'{a} & {b}'

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with poolcontext(processes=3) as pool:
        results = pool.map(merge_names_unpack, product(names, repeat=2))
    print(results)

Using functools.partial

For situations where one of the arguments remains constant across function calls, Python 2.7+ provides functools.partial. This allows you to fix certain parameters of your function, effectively reducing the number of arguments that need unpacking.

import multiprocessing
from functools import partial

def merge_names(a, b):
    return f'{a} & {b}'

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with multiprocessing.Pool(processes=3) as pool:
        results = pool.map(partial(merge_names, b='Sons'), names)
    print(results)

Conclusion

Using the multiprocessing library to apply functions with multiple arguments is straightforward once you understand how to use tools like starmap(), helper functions, and functools.partial. This tutorial has covered methods suitable for both modern and older versions of Python. By employing these techniques, you can efficiently parallelize your tasks and leverage the full power of multi-core processors.