Two functions in parallel with multiple arguments and return values

Question

I've got two separate functions. Each of them takes quite a long time to execute.

def function1(arg):
     do_some_stuff_here
     return result1

def function2(arg1, arg2, arg3):
     do_some_stuff_here
     return result2

I'd like to launch them in parallel, get their results (knowing which is which) and process the results afterwards. For what I've understood, multiprocessing is more efficient than Threading in Python 2.7 (GIL related issue). However I'm a bit lost whether it is better to use Process, Pool or Queue and how to implement them in a correct pythonic way for my use case.

Any help appreciated ;)

Answer 1

First of all, Process, Pool and Queue all have different use case.

Process is used to spawn a process by creating the Process object.

from multiprocessing import Process

def method1():
    print "in method1"
    print "in method1"

def method2():
    print "in method2"
    print "in method2"

p1 = Process(target=method1) # create a process object p1
p1.start()                   # starts the process p1
p2 = Process(target=method2)
p2.start()

Pool is used to parallelize execution of function across multiple input values.

from multiprocessing import Pool

def method1(x):
    print x
    print x**2
    return x**2

p = Pool(3)
result = p.map(method1, [1,4,9]) 
print result          # prints [1, 16, 81]

Queue is used to communicate between processes.

from multiprocessing import Process, Queue

def method1(x, l1):
    print "in method1"
    print "in method1"
    l1.put(x**2)
    return x

def method2(x, l2):
    print "in method2"
    print "in method2"
    l2.put(x**3)
    return x

l1 = Queue()
p1 = Process(target=method1, args=(4, l1, ))  
l2 = Queue()
p2 = Process(target=method2, args=(2, l2, )) 
p1.start()   
p2.start()      
print l1.get()          # prints 16
print l2.get()          # prints 8

Now, for your case you can use Process & Queue(3rd method) or you can manipulate the pool method to work (below)

import itertools
from multiprocessing import Pool
import sys

def method1(x):         
    print x
    print x**2
    return x**2

def method2(x):        
    print x
    print x**3
    return x**3

def unzip_func(a, b):  
    return a, b    

def distributor(option_args):
    option, args = unzip_func(*option_args)    # unzip option and args 

    attr_name = "method" + str(option)            
    # creating attr_name depending on option argument

    value = getattr(sys.modules[__name__], attr_name)(args) 
    # call the function with name 'attr_name' with argument args

    return value


option_list = [1,2]      # for selecting the method number
args_list = [4,2]        
# list of arg for the corresponding method, (argument 4 is for method1)

p = Pool(3)              # creating pool of 3 processes

result = p.map(distributor, itertools.izip(option_list, args_list)) 
# calling the distributor function with args zipped as (option1, arg1), (option2, arg2) by itertools package
print result             # prints [16,8]

Hope this helps.