How Can I Run Python Functions in Parallel Using Multiprocessing?

Parallel Execution of Functions

In Python, sequential execution of functions is the default behavior. However, when dealing with resource-intensive operations, it can be beneficial to run multiple functions concurrently to improve performance. This article discusses how to run functions in parallel using threading or multiprocessing.

Threading and multiprocessing are two libraries that provide mechanisms for multithreading and multiprocess scenarios, respectively. While threading is more lightweight, multiprocessing generally provides better performance for computational tasks.

Let's consider an example where we want to execute two functions, func1 and func2, in parallel. Using multiprocessing, we can define the functions as:

def func1():
    # Function 1 code
    print("func1: completing")


def func2():
    # Function 2 code
    print("func2: completing")

To run the functions in parallel, we can use the following approach:

from multiprocessing import Process

p1 = Process(target=func1)
p1.start()
p2 = Process(target=func2)
p2.start()

The start() method initiates the processes, and they execute concurrently. To ensure that our main process waits for the child processes to complete, we can use the join() method:

p1.join()
p2.join()

Additionally, we can encapsulate the parallel execution into a helper function:

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)

This allows us to run multiple functions in parallel with a simple and concise function call.

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