Going beyond a single CPU – implementing multiprocessing

We have seen the complexity of multithreaded programming and its limitations. The question is whether the complexity of multithreading is worth the effort. It may be worth it for I/O-related tasks but not for general application use cases, especially when an alternative approach exists. The alternative approach is to use multiprocessing because separate Python processes are not constrained by the GIL and execution can happen in parallel. This is especially beneficial when applications run on multicore processors and involve intensive CPU-demanding tasks. In reality, the use of multiprocessing is the only option in Python's built-in libraries to utilize multiple processor cores.

Graphics Processing Units (GPUs) provide a greater number of cores than regular CPUs and are considered more suitable for data processing tasks, especially when executing them in parallel. The only caveat is that in order to execute a data...