(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.

  • @[email protected]English
    3•14 hours ago

    IINM whether it’s “true” parallelism depends on the number of hardware cores (which shouldn’t be a problem nowadays). A single, physical core means concurrency (even with “hyper threading”) and multiple cores could mean parallelism. I can’t remember if threads are core bound or not. Processes can bound to cores on linux (on other OSes too most likely).

    So I suppose this is the preferred way to do concurrency, there is no async/await

    Python does have async which is syntax sugar for coroutines to be run in threads or processes using an executor (doc). The standard library has asyncio which describes valuable usecases for async/await in python.

    and you won’t use At “just” for a bit of concurrency. Right ?

    Is “At” a typo?

    We learn a little bit everyday. Thanks!

    You’re welcome :) I discovered the GIL the hard way unfortunately. Making another person aware of its existence to potentially save them some pain is worth it.

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    • Fred
      2•12 hours ago

      I can’t remember if threads are core bound or not.

      On Linux, by default they’re not. getcpu(2) says:

         The getcpu() system call identifies the processor and node on which the
   calling thread or process is currently running and writes them into the
   integers pointed to by the cpu and node arguments.  ...

   The  information  placed in cpu is guaranteed to be current only at the
   time of the  call:  unless  the  CPU  affinity  has  been  fixed  using
   sched_setaffinity(2),  the  kernel  might  change  the CPU at any time.
   (Normally this does not happen because the scheduler tries to  minimize
   movements  between  CPUs  to keep caches hot, but it is possible.)  The
   caller must allow for the possibility that the information returned  in
   cpu and node is no longer current by the time the call returns.
      • @[email protected]English
        2•12 hours ago

        Thank you. That’s good to know. In my OS architecture lectures, we were introduced to an OS with core bound threads. I can’t remember if it was a learning OS or something that really existed, hence my doubts.

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    • @[email protected]
      2•12 hours ago

      and you won’t use At “just” for a bit of concurrency. Right ?

      Is “At” a typo?

      Yes I wanted to talk about the Qt Framework. But with that much ways to do concurrency in the language’s core, I suspect you would use this framework for more than just its signal/slots feature. Like if you want their data structures, their network or GUI stack, …

      I’m not using Python, but I love to know the quirks of each languages.