Python Multithreading vs Multiprocessing vs Sequential Execution

Question

I have the below code:

import time
from threading import Thread
from multiprocessing import Process 

def fun1():

 for _ in xrange(10000000):
        print 'in fun1'
        pass

def fun2():

 for _ in xrange(10000000):
        print 'in fun2'
        pass

def fun3():

 for _ in xrange(10000000):
        print 'in fun3'
        pass

def fun4():

 for _ in xrange(10000000):
        print 'in fun4'
        pass

if __name__ == '__main__':

  #t1 = Thread(target=fun1, args=())
  t1 = Process(target=fun1, args=())
  #t2 = Thread(target=fun2, args=())
  t2 = Process(target=fun2, args=())
  #t3 = Thread(target=fun3, args=())
  t3 = Process(target=fun3, args=())
  #t4 = Thread(target=fun4, args=())
  t4 = Process(target=fun4, args=())
  t1.start()
  t2.start() 
  t3.start() 
  t4.start()
  start = time.clock()
  t1.join()
  t2.join()
  t3.join()
  t4.join()
  end = time.clock()
  print("Time Taken = ",end-start)

  '''
  start = time.clock()
  fun1()
  fun2()
  fun3()
  fun4()
  end = time.clock()
  print("Time Taken = ",end-start)
  '''

I ran the above program in three ways:

First Sequential Execution ALONE(look at the commented code and comment the upper code)
Second Multithreaded Execution ALONE
Third Multiprocessing Execution ALONE

The observations for end_time-start time are as follows:

Overall Running times

('Time Taken = ', 342.5981313667716) --- Running time by threaded execution
('Time Taken = ', 232.94691744899296) --- Running time by sequential Execution
('Time Taken = ', 307.91093406618216) --- Running time by Multiprocessing execution

Question :

I see sequential execution takes least time and Multithreading takes highest time. Why? I am unable to understand and also surprised by results.Please clarify.

Since this is a CPU intensive task and GIL is acquired, my understanding was Multiprocessing would take least time while threaded execution would take highest time.Please validate my understanding.

CoMartel · Accepted Answer · 2016-08-26 06:46:09Z

You use time.clock, wich gave you CPU time and not real time : you can't use that in your case, as it gives you the execution time (how long did you use the CPU to run your code, wich will be almost the same time for each of these case)

Running your code with time.time() instead of time.clock gave me these time on my computer :

Process : ('Time Taken = ', 5.226783990859985)
seq : ('Time Taken = ', 6.3122560000000005)
Thread :  ('Time Taken = ', 17.10062599182129)

The task given here (printing) is so fast that the speedup from using multiprocessing is almost balanced by the overhead.

For Threading, as you can only have one Thread running because of the GIL, you end up running all your functions sequentially BUT you had the overhead of threading (changing threads every few iterations can cost up to several milliseconds each time). So you end up with something much slower.

Threading is usefull if you have waiting times, so you can run tasks in between.

Multiprocessing is usefull for computationnally expensive tasks, if possible completely independant (no shared variables). If you need to share variables, then you have to face the GIL and it's a little bit more complicated (but not impossible most of the time).

EDIT : Actually, using time.clock like you did gave you the information about how much overhead using Threading and Multiprocessing cost you.

hainguyen · Accepted Answer · 2016-08-26 06:57:05Z

0

Basically you're right. What platform do you use to run the code snippet? I guess Windows. Be noticed that "print" is not CPU bound so you should comment out "print" and try to run it on Linux to see the difference (It should be what you expect). Use code like this:

def fun1():

 for _ in xrange(10000000):
        # No print, and please run on linux
        pass

answered Aug 26, 2016 at 6:57

hainguyen

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