How does Python multiprocessing bypass the GIL for CPU-bound tasks?

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How does Python multiprocessing bypass the GIL for CPU-bound tasks?

Asked on Oct 20, 2025

Answer

Python's Global Interpreter Lock (GIL) can be a bottleneck for CPU-bound tasks in multi-threaded programs. However, the `multiprocessing` module in Python allows you to bypass the GIL by creating separate processes, each with its own Python interpreter and memory space, enabling true parallel execution on multiple CPU cores.

Example Concept: The `multiprocessing` module in Python creates separate processes for each task, allowing them to run concurrently on multiple CPU cores. Each process has its own Python interpreter and memory space, which means the GIL is not shared between them, thus enabling true parallel execution for CPU-bound tasks. This is particularly useful for tasks that require significant computation and can benefit from parallel processing.

Additional Comment:

Use `multiprocessing.Pool` to manage multiple worker processes efficiently.
Processes are heavier than threads, so consider overhead when designing your application.
Data sharing between processes can be achieved using `multiprocessing.Queue` or `multiprocessing.Pipe`.
For I/O-bound tasks, consider using `asyncio` or threading instead of multiprocessing.

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