How do GIL and multiprocessing compare in Python?

How do GIL and multiprocessing compare in Python?

The Global Interpreter Lock (GIL) allows only one thread to execute at a time in CPython, which can limit performance in CPU-bound tasks. Multiprocessing, on the other hand, creates separate processes, bypassing the GIL and utilizing multiple CPU cores.

Can you give an example of when to use multiprocessing over threading?

Sure! If you're working on a CPU-bound task, like image processing or data analysis, multiprocessing is preferable. For I/O-bound tasks, like web scraping or file operations, threading might be more efficient.

What are the downsides of using multiprocessing?

Some downsides include higher memory usage since each process has its own memory space, and the overhead of inter-process communication can slow down performance for certain tasks.

Is there a way to measure the performance difference?

Yes, you can use the time module to measure execution time for both threading and multiprocessing implementations of the same task. This will give you a clear comparison of their performance.