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*CPU-bound tasks* are where single threading in Python really starts to show its limitations. These tasks are those that spend the majority of their time doing heavy calculations, such as image processing, scientific simulations, or complex mathematical operations. Because the *Global Interpreter Lock (GIL)* in Python allows only one thread to execute Python bytecode at a time, your program won't be able to take full advantage of multi-core processors. Even if you try to use threading to speed up CPU-bound tasks, the GIL will serialize their execution, preventing true parallelism. This means that, on a multi-core processor, a single-threaded Python program might run just as fast (or even slower, due to thread overhead) as a multithreaded program trying to perform the same CPU-bound tasks. This can be a major bottleneck if your application needs to process large datasets or perform intensive computations. In these cases, you might want to consider alternatives, such as multiprocessing (which bypasses the GIL by creating separate processes) or using libraries like NumPy or libraries that release the GIL to perform calculations in parallel.
