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