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Parallel processing allows a computer to perform multiple tasks simultaneously, which can significantly speed up the execution of programs. It involves dividing a problem into smaller sub-problems that can be solved concurrently. Here's a high-level overview of how it works:

Task Division: The main problem is split into smaller, independent tasks.

Distribution: These tasks are distributed across multiple processors or cores.

Execution: Each processor or core works on its assigned task simultaneously.

Coordination: The results from the different processors are combined to form the final output.

In Python, you can use the multiprocessing module to achieve parallel processing.

Parallel processing is particularly useful for tasks like data analysis, scientific simulations, and complex computations where multiple processors can work together to complete the work faster. A personal example of this is the optimized version of my "Duplicate Video File Finder" script. Maybe you have a script that has to wait a while for a response or result from some function or something, then it continues to the rest of the code. Instead of doing this in serious, you can execute the other parts of the code while that function does its thing. This is heavier on your CPU though and it requires multiple different cores to work properly.