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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[–] [email protected] 0 points 2 months ago (1 children)

Speed-wise, multiple processes and multiple threads should be identical, if you are using the same primitives (shared memory, system-wide semaphore).

Threads are easier to use and use less RAM, because all your memory is shared automatically, and system-wide semaphores have complicated API.

[–] [email protected] 1 points 2 months ago (1 children)

On python, because of the Gil, multi processing should always be preferred if possible.

[–] logging_strict 1 points 1 month ago

Also logging is not isolated. Bleeds all over the place. Which is a deal breaker

Not worth the endless time doing forensics

Agree! Lets stick with multiprocessing

one thread sounds nice. Lets do much more of that