I'm new towards multiprocessing concept.
from multiprocessing import Process
def square(x):
for x in numbers:
print('%s squared is %s' % (x, x**2))
if __name__ == '__main__':
numbers = [43, 50, 5, 98, 34, 35]
p = Process(target=square, args=('x',))
p.start()
p.join
print "Done"
Done
43 squared is 1849
50 squared is 2500
5 squared is 25
98 squared is 9604
34 squared is 1156
35 squared is 1225
I understand, we can use multiprocessing.cpu_count()
to get number of cpu in system
However, I failed in achieving 2 interested things. -
There are several things amiss in your example.
p.join()
, so the process is never waited for (which is why Done
is printed first). You should instead be using multiprocessing.Pool
, something like this.
from multiprocessing import Pool
def square(x):
print('%s squared is %s' % (x, x**2))
if __name__ == '__main__':
numbers = range(1, 1000, 50)
with Pool() as p:
for value in p.imap_unordered(square, numbers):
# You could do something with the
# return value from `square` here.
pass
print("Done")
This outputs (eg – the order is not guaranteed)
1 squared is 1
51 squared is 2601
101 squared is 10201
151 squared is 22801
201 squared is 40401
251 squared is 63001
401 squared is 160801
451 squared is 203401
501 squared is 251001
301 squared is 90601
551 squared is 303601
601 squared is 361201
351 squared is 123201
651 squared is 423801
701 squared is 491401
751 squared is 564001
801 squared is 641601
851 squared is 724201
901 squared is 811801
951 squared is 904401
Done
Pool()
defaults to using cpu_count
processes, so you don't need to worry about that. square()
only processes one number now. It should really return it for printing and processing, not print it by itself, but this is a simple example. .map()
, .imap()
or some of the other methods on Pool
instead; I chose .imap_unordered()
because I don't care about the order in which I get those values (and besides, I'm doing nothing with them anyway). Nothing in particular "locks" a single process to a single CPU, though – after all, a single process could be utilizing multiple threads, which the OS scheduler might schedule onto different CPUs. Different OSes have APIs to limit the processors for each process (and thread) though; if you really need that, you can dig into those.
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