How does Python's multiprocessing.Pool perform differently on MacOS and Linux operating systems?

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Python's multiprocessing.Pool is a powerful library used to perform parallel processing in Python. However, it has been noted that there are differences in its performance on macOS and Linux operating systems.

On Linux, multiprocessing.Pool uses the fork() function to create child processes, which are exact copies of the parent process. These child processes are able to run in parallel with the main process, allowing for efficient use of resources. The fork() function is very efficient on Linux as it creates a copy-on-write snapshot of the original process, instead of duplicating its resources.

On macOS, however, multiprocessing.Pool uses the spawn() function instead of fork(). This is because macOS does not support the fork() function used in Linux. Instead, spawn() creates a new process for each worker in the pool. These new processes are separate from the main process and do not share any data. This can result in increased overhead and reduced performance, especially for large datasets.

In summary, the performance of multiprocessing.Pool differs on macOS and Linux due to the underlying operating system architecture. Linux is optimized for efficient process creation using the fork() function while macOS uses the spawn() function, which can result in increased overhead and reduced performance.