線程適用於I/O密集型任務，因共享內存且無需真正並行計算；1.線程通過共享內存簡化通信，但受GIL限制無法並行執(zhí)行CPU任務；2.多進程繞過GIL實現(xiàn)真正的並行，適合CPU密集型任務但需額外處理進程間通信；3.選擇依據(jù)是任務類型：等待外部資源用線程，計算密集型用多進程；4.線程開銷小但易引發(fā)競態(tài)條件，多進程更重且需考慮數(shù)據(jù)序列化問題。

Threading and multiprocessing both let Python handle multiple tasks, but they work differently under the hood and suit different types of tasks.

1. Threading in Python – Simultaneous-ish, not truly parallel

Threading gives the illusion of doing things at the same time, especially useful for I/O-bound tasks like downloading files or waiting for user input.

• Threads share the same memory space, so communication between them is straightforward.
• However, due to Python's Global Interpreter Lock (GIL), only one thread executes Python bytecode at a time — meaning threads don't run CPU-heavy tasks in parallel.
• Great for tasks where you're waiting on external resources (like network requests or disk reads).

Example use case:
You want to download 5 web pages at once. Threading can help speed this up because most of the time is spent waiting for responses from servers.

Common tools:
Use threading module or higher-level interfaces like concurrent.futures.ThreadPoolExecutor .

2. Multiprocessing – Real parallelism, separate processes

Multiprocessing spawns separate Python processes, each with its own interpreter and memory space. This bypasses the GIL and allows true parallel execution.

• Ideal for CPU-bound tasks like image processing, calculations, or data crunching.
• Because processes don't share memory by default, you need to use special tools ( multiprocessing.Queue , Manager , etc.) if you want them to communicate.
• Heavier than threads — starting a new process takes more time and resources.

Example use case:
You're running a machine learning model that needs to process large datasets — multiprocessing lets you fully utilize all your CPU cores.

Common tools:
Use multiprocessing module or concurrent.futures.ProcessPoolExecutor .

3. When to choose which?

Here's a quick guide to help you pick:

• Use threading when:

  • Your program spends time waiting (network, disk, user input).
  • You want simpler shared state between tasks.
  • You're not pushing the CPU hard.

• Use multiprocessing when:

  • Your code is doing heavy computations.
  • You want to take advantage of multiple CPU cores.
  • Each task can run independently without much coordination.

A rule of thumb:
If it's about waiting, go threading. If it's about calculating, go multiprocessing.

4. Overhead and Gotchas

• Starting a new process is slower than creating a thread.
• Inter-process communication (IPC) is more complex and slower than sharing variables between threads.
• On Windows, multiprocessing requires functions and data to be picklable — which isn't always straightforward.
• Threaded programs can suffer from race conditions if multiple threads access and modify shared data simultaneously.

So depending on what your program does — waiting or computing — threading or multiprocessing will make more sense. Neither is universally better; it's about choosing the right tool for the job.

基本上就這些。

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