The multiprocessing API was originally designed to mimic the thread API. However, it has evolved, and in recent versions of Python 3, it supports more features more robustly. The multiprocessing library is designed for when CPU-intensive jobs need to happen in parallel and multiple cores are available (almost all computers, even a little smartwatch, have multiple cores). Multiprocessing is not useful when the processes spend a majority of their time waiting on I/O (for example, network, disk, database, or keyboard), but it is the way to go for parallel computation.
The multiprocessing module spins up new operating system processes to do the work. This means there is an entirely separate copy of the Python interpreter running for each process. Let's try to parallelize a compute-heavy operation using similar constructs to those provided by the threading API...
