Python lets you parallelize workloads using ... This example is I/O bound, so there’s likely to be no discernible performance difference between using threads or processes; still, the basic ...

Python's multiprocessing module is commonly used for parallelism, as it allows different processes to run in isolation on separate cores, bypassing the limitations of the Global Interpreter Lock (GIL) ...

The multiprocessing module, for instance, makes it possible to run concurrent instances of the Python interpreter (each on its own physical thread) and share work between them. However ...