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Multiprocessing: The Parallel Computing Tool That Makes Python Code Fly!

In the world of Python, some tasks are as slow as a snail, even though the machine’s performance is strong, they run incredibly slowly. Today, I want to introduce you to a magical tool that can significantly enhance code performance – Multiprocessing. Imagine your program is no longer a single-threaded tortoise, but instead works like a “hundred-armed giant” simultaneously; this is the magic of multi-processing!

What is Multiprocessing?

Multi-processing sounds impressive, but it simply means letting multiple CPU cores of the computer work together. Traditional Python code defaults to single-process, like one person doing the work. Multiprocessing summons multiple “clones” to work in parallel, greatly improving computational efficiency.

Process vs Thread: Confusing the Two

Many people easily confuse processes and threads. To put it simply, a process is like an independent factory, while a thread is like different workers in that factory. Processes have independent memory space, while threads share memory. For compute-intensive tasks, multi-processing often has advantages over multi-threading.

Getting Started with Multiprocessing

Let’s see how to use Multiprocessing to achieve parallel computing:

from multiprocessing import Process
import os
def worker(name):
    print(f"Child process {name} is running, process ID is: {os.getpid()}")
if __name__ == '__main__':
    processes = []
    for i in range(5):
        p = Process(target=worker, args=(f'Process-{i}',))
        processes.append(p)
        p.start()
    for p in processes:
        p.join()

This code will simultaneously create 5 processes, each printing its name and process ID. Isn’t that super cool?

Pooling: A More Advanced Approach

Pool is a more powerful weapon in Multiprocessing. Imagine it as a “process pool” that can automatically allocate tasks:

from multiprocessing import Pool
def square(x):
    return x * x
if __name__ == '__main__':
    with Pool(processes=4) as pool:
        results = pool.map(square, [1, 2, 3, 4, 5])
        print(results)  # Output: [1, 4, 9, 16, 25]

Friendly Reminder

While multi-processing is powerful, it is not a panacea. In some scenarios, the overhead of creating processes may be greater than executing directly. Choosing multi-processing should depend on the specific computational tasks and scenarios.

Multiprocessing is that powerful: it makes your Python code take off like a rocket! Go try it out, and it will surely double your code efficiency!

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