Threads are suitable for I/O-intensive tasks, because shared memory does not require real parallel computing; 1. Threads simplify communication through shared memory, but cannot execute CPU tasks in parallel due to GIL restrictions; 2. Multiple processes bypass GIL to achieve true parallelism, suitable for CPU-intensive tasks but need additional processing of inter-process communication; 3. The selection basis is the task type: threads are used to wait for external resources, and multi-processes are used to calculate intensive; 4. Thread overhead is small but prone to cause race conditions, multi-processes are heavier and data serialization issues need to be considered.
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 allow 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 computings.
- 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.
Basically that's it.
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