Multiple process or threads? And, why? (programming.dev)

submitted 1 month ago by anzo@programming.dev to c/python@programming.dev

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(For context, I'm basically referring to Python 3.12 "multiprocessing.Pool Vs. concurrent.futures.ThreadPoolExecutor"...)

Today I read that multiple cores (parallelism) help in CPU bound operations. Meanwhile, multiple threads (concurrency) is due when the tasks are I/O bound.

Is this correct? Anyone cares to elaborate for me?

At least from a theorethical standpoint. Of course, many real work has a mix of both, and I'd better start with profiling where the bottlenecks really are.

If serves of anything having a concrete "algorithm". Let's say, I have a function that applies a map-reduce strategy reading data chunks from a file on disk, and I'm computing some averages from these data, and saving to a new file.

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[-] jacksilver@lemmy.world 4 points 1 month ago

Threads all run on the same core, processes can run on different cores.

Because threads run on the same core, the only time they can improve performance is if there are non-cpu tasks in your code - usually I/O operations. Otherwise the only thing multi threading can provide is the appearance of parallelism (as the cpu jumps back and forth between threads progressing each in small steps).

On the other hand, multiprocessing allows you to run code on different cores, meaning you can take full advantage of all your processing power. However, if youre program has a lot of I/O tasks, you might end up bottlenecked by the I/O and never see any improvements.

For the example you mentioned, it's likely threading would be the best as it's got a little less overhead, easier to program, and you're task is mostly I/O bound. However, if the calculations are relatively quick, it's possible you wouldn't see any improvement as the cpu would still end up waiting for the I/O.