this post was submitted on 26 Mar 2025
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python has way too many ways to do that.
asyncio,future,thread,multiprocessing...Of the ways you listed the only one that will actually take advantage of a multi core CPU is
multiprocessingyup, that's true. most meaningful tasks are io-bound so "parallel" basically qualifies as "whatever allows multiple threads of execution to keep going". if you're doing numbercrunching in pythen without a proper library like pandas, that can parallelize your calculations, you're doing it wrong.
I’ve used multiprocessing to squeeze more performance out of numpy and scipy. But yeah, resorting to multiprocessing is a sign that you should be dropping into something like Rust or a C variant.
Most numpy array functions already utilize multiple cores, because they're optimized and written in C
I've always hated object oriented multi threading. Goroutines (green threads) are just the best way 90% of the time. If I need to control where threads go I'll write it in rust.
nothing about any of those libraries dictates an OO approach.
Unless it's java.
Meh, even Java has decent FP paradigm support these days. Just because you can do everything in an OO way in Java doesn't mean you need to.
If I have to put a thread object in a variable and call a method on it to start it then it's OO multi threading. I don't want to know when the thread spawns, I don't want to know what code it's running, and I don't want to know when it's done. I just want shit to happen at the same time (90% of the time)
the thread library is aping the posix thread interface with python semantics.