I am working on multiprocessing in Python.
For example, consider the example given in the Python multiprocessing documentation (I have changed 100 to 1000000 in the example, just to consume more time). When I run this, I do see that Pool() is using all the 4 processes but I don't see each CPU moving upto 100%. How to achieve the usage of each CPU by 100%?
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4)
result = pool.map(f, range(10000000))
It is because multiprocessing requires interprocess communication between the main process and the worker processes behind the scene, and the communication overhead took more (wall-clock) time than the "actual" computation (x * x) in your case.
Try "heavier" computation kernel instead, like
def f(x):
return reduce(lambda a, b: math.log(a+b), xrange(10**5), x)
Update (clarification)
I pointed out that the low CPU usage observed by the OP was due to the IPC overhead inherent in multiprocessing but the OP didn't need to worry about it too much because the original computation kernel was way too "light" to be used as a benchmark. In other words, multiprocessing works the worst with such a way too "light" kernel. If the OP implements a real-world logic (which, I'm sure, will be somewhat "heavier" than x * x) on top of multiprocessing, the OP will achieve a decent efficiency, I assure. My argument is backed up by an experiment with the "heavy" kernel I presented.
@FilipMalczak, I hope my clarification makes sense to you.
By the way there are some ways to improve the efficiency of x * x while using multiprocessing. For example, we can combine 1,000 jobs into one before we submit it to Pool unless we are required to solve each job in real time (ie. if you implement a REST API server, we shouldn't do in this way).
You're asking wrong kind of question. multiprocessing.Process represents process as understood in your operating system. multiprocessing.Pool is just a simple way to run several processes to do your work. Python environment has nothing to do with balancing load on cores/processors.
If you want to control how will processor time be given to processes, you should try tweaking your OS, not python interpreter.
Of course, "heavier" computations will be recognised by system, and may look like they do just what you want to do, but in fact, you have almost no control on process handling.
"Heavier" functions will just look heavier to your OS, and his usual reaction will be assigning more processor time to your processes, but that doesn't mean you did what you wanted to - and that's good, because that the whole point of languages with VM - you specify logic, and VM takes care of mapping this logic onto operating system.
