TL;DR - implementing parallel code in Python means using multiprocessing as opposed to multithreading, but it depends on if you're CPU bound or I/O bound. The

TL;DR - implementing parallel code in Python means using multiprocessing as opposed to multithreading, but it depends on if you're CPU bound or I/O bound. The GIL prevents more than one instance of the python interpreter from running _per processor_. This effectively means you can only have one Python thread per processor running at a time, which is frustrating because that kinda defeats the point of threading. However, most python programs aren't CPU bound, but I/O bound. For those programs, using the python multithreading abstraction is fine; CPUs are fast enough to do context switches while one thread waits for data, etc. For CPU bound tasks, the solution is to write using the multiprocess module; each process spun up has its own interpreter and its own GIL. Things have changed somewhat with Python 3, where there are some more robust internal scheduling tools inside the multithreading module, but the rule of thumb still stays the same.