Secret Recipes of the Python Ninja

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The multiprocessing example demonstrates that, while multiprocessing is useful in some situations, considerable effort must be made to optimize the program to best utilize multiprocessing. In addition, multiprocessing can be slower than single thread operations because each process must be started anew, much like the function call overhead.

There is also the problem of each process taking over a CPU core. While this helps with the parallelization of code processing, it means that the core is essentially locked until the processing is done.

For counts under than 1 million, multiprocessing performed well. At 100,000 calls, the total time was just under 4 seconds. At 10,000 calls, the time was less than 0.5 seconds, which is ...