Implementing Process Forking and Parallel Processing in Python Using the `multiprocessing` Module

Introduction

In Python, there are two primary methods for forking processes and implementing parallel processing: the low-level os.fork() function and the high-level multiprocessing module. This article explains how to utilize these methods to achieve concurrent execution in Python applications.

Forking Processes with os.fork()

The os.fork() function is available on UNIX-based systems and provides a low-level approach to process creation. Calling this function duplicates the current process, creating a new child process.

import os

pid = os.fork()

if pid > 0:
    # Parent process execution
    print("This is the parent process. Child PID is", pid)
else:
    # Child process execution
    print("This is the child process.")

Utilizing the multiprocessing Module

The multiprocessing module offers a high-level API for parallel processing in Python, facilitating the creation of independent processes, data sharing, and inter-process communication with ease.

Basic Usage

from multiprocessing import Process

def print_hello():
    print("Hello from a child process")

if __name__ == '__main__':
    p = Process(target=print_hello)
    p.start()
    p.join()

Sharing Data Between Processes

The multiprocessing module provides mechanisms like Value and Array for sharing data between processes.

from multiprocessing import Process, Value

def increment(value):
    value.value += 1

if __name__ == '__main__':
    num = Value('i', 0)
    p = Process(target=increment, args=(num,))
    p.start()
    p.join()

    print("Incremented value:", num.value)

Conclusion

Python offers two main avenues for implementing concurrent processing: the os.fork() function and the multiprocessing module. While os.fork() allows for finer control in low-level processes, multiprocessing is more suited for general purposes due to its ease of use in inter-process communication and data sharing.