On Sun, 20 Feb 2022 18:05:33 +0000, Shaozhong SHI <shishaozh...@gmail.com>
declaimed the following:
>I am trying this approach,
>
>import multiprocessing as mp
>
>def my_func(x):
> print(x**x)
>
Not much of a processing load there, especially for your small set of
integers. I suspect you are consuming a significant time just having the OS
create and tear down each process.
In truth, for that example, I suspect plain threads will run faster
because you don't have the overhead of setting up a process with new
stdin/stdout/stderr. The exponential operation probably runs within one
Python threading quantum, and the print() will trigger a thread switch to
allow the next one to compute.
>def main():
> pool = mp.Pool(mp.cpu_count())
> result = pool.map(my_func, [4,2,3])
-=-=-
>>> import multiprocessing as mp
>>> mp.cpu_count()
8
>>>
-=-=-
Really under-loaded on my Win10 system (hyperthreaded processors count
as 2 CPUs, so a quad-core HT reports as 8 CPUs). Even an older
Raspberry-Pi 3B (quad core) reports
-=-=-
md_admin@microdiversity:~$ python3
Python 3.7.3 (default, Jan 22 2021, 20:04:44)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import multiprocessing as mp
>>> mp.cpu_count()
4
>>> exit()
md_admin@microdiversity:~$
-=-=-
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