Python的全局解釋器鎖（GIL）阻止了多線程執(zhí)行CPU密集型任務(wù)的並行性，但multiprocessing模塊通過創(chuàng)建獨立進程繞過GIL實現(xiàn)真正的並行。 1. multiprocessing為每個進程分配獨立的Python解釋器和內(nèi)存空間，避免GIL限制；2. 適用於CPU密集型任務(wù)如數(shù)據(jù)處理、圖像操作等；3. 使用Process類或Pool類可快速啟動多進程任務(wù)；4. 相比線程，進程開銷更大，適合計算量大的場景；5. 需注意進程間通信複雜性和內(nèi)存佔用問題。因此，在需要跨多核並行執(zhí)行時應(yīng)優(yōu)先使用multiprocessing而非threading。

Python's Global Interpreter Lock (GIL) is a mutex that ensures only one thread executes Python bytecode at a time, even on multi-core systems. This means that while threads can be useful for I/O-bound tasks, they don't help with CPU-bound work due to the GIL.

But if you're looking to truly run code in parallel across multiple CPUs, Python's multiprocessing module is your go-to tool.

How multiprocessing works around the GIL

The key idea behind multiprocessing is that it spawns separate processes , each with its own Python interpreter and memory space. Since each process has its own GIL, this allows true parallel execution of Python code across multiple CPU cores.

Unlike threading, where threads share the same memory, multiprocessing avoids the GIL bottleneck by not sharing the interpreter — each process runs independently.

For example:

• If you have a dual-core CPU, you can spawn two worker processes.
• Each can crunch numbers simultaneously without stepping on each other due to the GIL.

This makes multiprocessing ideal for CPU-bound operations like data processing, image manipulation, or scientific computations.

When to use multiprocessing instead of threading

You should consider using multiprocessing when:

• Your task is CPU-intensive — things like calculations, transformations, or heavy data processing.
• You want true parallelism across multiple CPU cores.
• The overhead of spawning new processes is acceptable compared to the gains from parallel computation.

On the flip side, for I/O-bound tasks — such as reading/writing files, waiting for network responses, or handling user input — threads are usually better because they're lighter weight and avoid the overhead of process creation.

So here's a quick rule of thumb:

• Use threads for I/O-bound work.
• Use multiprocessing for CPU-bound work.

Basic usage: Getting started with multiprocessing

The most straightforward way to start using multiprocessing is through the Process class.

Here's a minimal example:

 from multiprocessing import Process
import os

def print_pid():
    print(f"Running in process {os.getpid()}")

if __name__ == "__main__":
    p1 = Process(target=print_pid)
    p2 = Process(target=print_pid)
    p1.start()
    p2.start()
    p1.join()
    p2.join()

Each call to Process() starts a new Python process, so each runs under its own GIL. That means both print_pid() calls could execute at the same time on different CPU cores.

If you're working with functions that return values, you'll probably want to use Pool , which simplifies managing multiple workers:

 from multiprocessing import Pool

def square(x):
    return x * x

if __name__ == "__main__":
    with Pool(4) as p:
        result = p.map(square, [1, 2, 3, 4, 5])
    print(result)

This will distribute the list [1,2,3,4,5] across four worker processes in parallel.

A few tips when starting out:

• Always protect your main code with if __name__ == "__main__": on Windows.
• Be careful with shared state — inter-process communication adds complexity.
• For simple parallel loops, Pool.map() is often all you need.

A few gotchas to keep in mind

Multiprocessing isn't magic — there are some trade-offs.

• Startup overhead : Processes take longer to start than threads. So for small tasks, the extra time might not be worth it.
• Memory usage : Each process gets its own memory, which can add up quickly.
• Inter-process communication (IPC) : Sharing data between processes requires special tools like Queue , Pipe , or shared memory ( Value , Array ), which are more complex than just passing variables.

Also, on macOS and Linux, the default method for spawning processes changed to "spawn" in newer Python versions, which affects how global variables and imported modules behave.

In short: it's powerful, but not always the easiest to debug or optimize.

That's the core of using Python's multiprocessing module effectively. It's not overly complicated, but understanding when and how to apply it makes a big difference in performance.

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