Differentiation of Python Multiprocessing

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In this article, we will learn how we can achieve multiprocessing using Python. We also discuss its advanced concepts.

What is Multiprocessing?

Multiprocessing is the ability of the system to run one or more processes in parallel. In simple words, multiprocessing uses the two or more CPU within the single computer system. multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. This method is also capable to allocate the tasks between more than one process. Processing units share the main memory and peripherals to process programs simultaneously. Multiprocessing Application breaks into smaller parts and runs independently. Each process is allocated to the processor by the operating system. Python provides the built-in package called multiprocessing which supports swapping processes. Before working with the multiprocessing, we must aware with the process object.

Multiprocessing | Learning Concurrency in Python

Why Multiprocessing?

Multiprocessing is essential to perform the multiple tasks within the Computer system. Suppose a computer without multiprocessing or single processor. We assign various processes to that system at the same time. It will then have to interrupt the previous task and move to another to keep all processes going. It is as simple as a chef is working alone in the kitchen. He has to do several tasks to cook food such as cutting, cleaning, cooking, kneading dough, baking, etc. Therefore, multiprocessing is essential to perform several task at the same time without interruption. It also makes easy to track all the tasks. That is why the concept of multiprocessing is to arise.

  • Multiprocessing can be represented as a computer with more than one central processor.
  • A Multi-core processor refers to single computing component with two or more independent units.

In the multiprocessing, the CPU can assign multiple tasks at one each task has its own processor.

Multiprocessing In Python

Python provides the multiprocessing module to perform multiple tasks within the single system. It offers a user-friendly and intuitive API to work with the multiprocessing. Let’s understand the simple example of multiple processing.

Example –

from multiprocessing import Process  
   def disp():        print ('Hello !! Welcome to Python Tutorial')   
  if __name__ == '__main__':   
  p = Process(target=disp)    


'Hello !! Welcome to Python Tutorial'


In the above code, we have imported the Process class then create the Process object within the disp() function. Then we started the process using the start() method and completed the process with the join() method. We can also pass the arguments in the declared function using the args keywords. Let’s understand the following example of the multiprocessing with arguments.

Example – 2

# Python multiprocessing example 
 # importing the multiprocessing module import multiprocessing  def cube(n):    
# This function will print the cube of the given number
    print("The Cube is: {}".format(n * n * n)) def square(n):    
  # This function will print the square of the given number    
 print("The Square is: {}".format(n * n))  if __name__ == "__main__":      # creating two processes     process1 = multiprocessing.Process(target= square, args=(5, )) 
    process2 = multiprocessing.Process(target= cube, args=(5, ))      # Here we start the process 1    process1.start()
   # Here we start process 2     process2.start()    
# The join() method is used to wait for process 1 to complete    process1.join() 
   # It is used to wait for process 1 to complete    process2.join() 
    # Print if both processes are completed 
    print("Both processes are finished")  


The Cube is: 125
The Square is: 25
Both processes are finished

Explanation –

In the above example, We created the two functions – the cube() function calculates the given number’s cube, and the square() function calculates the square of the given number. Next, we defined the process object of the Process class that has two arguments. The first argument is a target that represents the function to be executed, and the second argument is args that represents the argument to be passed within the function.

  1. process1 = multiprocessing.Process(target= square, args=(5, ))  
  2. process2 = multiprocessing.Process(target= cube, args=(5, ))  

We have used the start() method to start the process.

  1. process1.start()  
  2. process2.start()  

As we can see in the output, it waits to completion of process one and then process 2. The last statement is executed after both processes are finished.

Python Multiprocessing Classes

Python multiprocessing module provides many classes which are commonly used for building parallel program. We will discuss its main classes – Process, Queue and Lock. We have already discussed the Process class in the previous example. Now we will discuss the Queue and Lock classes. Let’s see the simple example of a get number of CPUs currently in the system.

Example –

import multiprocessing  print("The number of CPU currently working in system :
 ", multiprocessing.cpu_count())  


('The number of CPU currently woking in system : ', 32)

The above number of CPUs can vary for your pc. For us, the number of cores is 32.

Concurrency and parallelism

Concurrency means that two or more calculations happen within the same time frame. Parallelism means that two or more calculations happen at the same moment. Parallelism is therefore a specific case of concurrency. It requires multiple CPU units or cores. True parallelism in Python is achieved by creating multiple processes, each having a Python interpreter with its own separate GIL. Python has three modules for concurrency: multiprocessingthreading, and asyncio. When the tasks are CPU intensive, we should consider the multiprocessing module. When the tasks are I/O bound and require lots of connections, the asyncio module is recommended. For other types of tasks and when libraries cannot cooperate with asyncio, the threading module can be considered.

Embarrassinbly parallel

The term embarrassinbly parallel is used to describe a problem or workload that can be easily run in parallel. It is important to realize that not all workloads can be divided into subtasks and run parallelly. For instance those, who need lots of communication among subtasks.

The examples of perfectly parallel computations include:

  • Monte Carlo analysis
  • numerical integration
  • rendering of computer graphics
  • brute force searches in cryptography
  • genetic algorithms

Another situation where parallel computations can be applied is when we run several different computations, that is, we don’t divide a problem into subtasks. For instance, we could run calculations of π using different algorithms in parallel.

Python Multiprocessing Using Queue Class

We know that Queue is important part of the data structure. Python multiprocessing is precisely the same as the data structure queue, which based on the “First-In-First-Out” concept. Queue generally stores the Python object and plays an essential role in sharing data between processes. Queues are passed as a parameter in the Process’ target function to allow the process to consume data. The Queue provides the put() function to insert the data and get() function to get data from the queues. Let’s understand the following example.

Example –

# Importing Queue Class  
from multiprocessing import Queue  
fruits = ['Apple', 'Orange', 'Guava', 'Papaya', 'Banana']  
count = 1  
# creating a queue object  
queue = Queue()  
print('pushing items to the queue:')  
for fr in fruits:  
    print('item no: ', count, ' ', fr)  
    count += 1  
print('\npopping items from the queue:')  
count = 0  
while not queue.empty():  
    print('item no: ', count, ' ', queue.get())  
    count += 1  


pushing items to the queue:
('item no: ', 1, ' ', 'Apple')
('item no: ', 2, ' ', 'Orange')
('item no: ', 3, ' ', 'Guava')
('item no: ', 4, ' ', 'Papaya')
('item no: ', 5, ' ', 'Banana')

popping items from the queue:
('item no: ', 0, ' ', 'Apple')
('item no: ', 1, ' ', 'Orange')
('item no: ', 2, ' ', 'Guava')
('item no: ', 3, ' ', 'Papaya')
('item no: ', 4, ' ', 'Banana')

Explanation –

In the above code, we have imported the Queue class and initialized the list named fruits. Next, we assigned a count to 1. The count variable will count the total number of elements. Then, we created the queue object by calling the Queue() method. This object will used to perform operations in the Queue. In for loop, we inserted the elements one by one in the queue using the put() function and increased the count by 1 with each iteration of loop.

Python Multiprocessing Lock Class

The multiprocessing Lock class is used to acquire a lock on the process so that we can hold the other process to execute a similar code until the lock has been released. The Lock class performs mainly two tasks. The first is to acquire a lock using the acquire() function and the second is to release the lock using the release() function.

Python Multiprocessing Example

Suppose we have multiple tasks. So, we create two queues: the first queue will maintain the tasks, and the other will store the complete task log. The next step is to instantiate the processes to complete the task. As discussed previously, the Queue class is already synchronized, so we don’t need to acquire a lock using the Lock class.

In the following example, we will merge all the multiprocessing classes together. Let’s see the below example.

Example –

from multiprocessing import Lock, Process, Queue, current_process  
import time  
import queue   
def jobTodo(tasks_to_perform, complete_tasks):  
    while True:  
            # The try block to catch task from the queue.  
            # The get_nowait() function is used to  
            # raise queue.Empty exception if the queue is empty.  
            task = tasks_to_perform.get_nowait()  
        except queue.Empty:  
                # if no exception has been raised, the else block will execute  
                # add the task completion  
            complete_tasks.put(task + ' is done by ' + current_process().name)  
    return True  
def main():  
    total_task = 8  
    total_number_of_processes = 3  
    tasks_to_perform = Queue()  
    complete_tasks = Queue()  
    number_of_processes = []  
    for i in range(total_task):  
        tasks_to_perform.put("Task no " + str(i))  
    # defining number of processes  
    for w in range(total_number_of_processes):  
        p = Process(target=jobTodo, args=(tasks_to_perform, complete_tasks))  
    # completing process  
    for p in number_of_processes:  
    # print the output  
    while not complete_tasks.empty():  
    return True  
if __name__ == '__main__':  


Task no 2
Task no 5
Task no 0
Task no 3
Task no 6
Task no 1
Task no 4
Task no 7
Task no 0 is done by Process-1
Task no 1 is done by Process-3
Task no 2 is done by Process-2
Task no 3 is done by Process-1
Task no 4 is done by Process-3
Task no 5 is done by Process-2
Task no 6 is done by Process-1
Task no 7 is done by Process-3

Python Multiprocessing Pool

Python multiprocessing pool is essential for parallel execution of a function across multiple input values. It is also used to distribute the input data across processes (data parallelism). Consider the following example of a multiprocessing Pool.

Example –

from multiprocessing import Pool  
import time  
w = (["V", 5], ["X", 2], ["Y", 1], ["Z", 3])  
def work_log(data_for_work):  
    print(" Process name is %s waiting time is %s seconds" % (data_for_work[0], data_for_work[1]))  
    print(" Process %s Executed." % data_for_work[0])  
def handler():  
    p = Pool(2)  
    p.map(work_log, w)  
if __name__ == '__main__':  


Process name is V waiting time is 5 seconds
Process V Executed.
Process name is X waiting time is 2 seconds
Process X Executed.
Process name is Y waiting time is 1 seconds
Process Y Executed.
Process name is Z waiting time is 3 seconds
Process Z Executed.

Let’s understand another example of the multiprocessing Pool.

Example – 2

from multiprocessing import Pool  
def fun(x):  
    return x*x  
if __name__ == '__main__':  
    with Pool(5) as p:  
        print(p.map(fun, [1, 2, 3]))  


[1, 8, 27]

Proxy Objects

The proxy objects are referred to as shared objects which reside in a different process. This object is also called as a proxy. Multiple proxy objects might have a similar referent. A proxy object consists of various methods which are used to invoked corresponding methods of its referent. Below is the example of proxy objects.

Example –

from multiprocessing import Manager  
manager = Manager()  
l = manager.list([i*i for i in range(10)])  


[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
<ListProxy object, typeid 'list' at 0x7f063621ea10>
[4, 9, 16]

The proxy objects are picklable so we can pass them between processes. These objects are also used for level of control over the synchronization.

Commonly Used Functions of Multiprocessing

So far, we have discussed the basic concepts of multiprocessing using Python. Multiprocessing is a broad topic itself and essential for performing various tasks within a single system. We are defining a few essential functions that are commonly used to achieve multiprocessing.

pipe()The pipe() function returns a pair of connection objects.
run()The run() method is used to represent the process activities.
start()The start()method is used to start the process.
join([timeout])The join() method is used to block the process until the process whose join() method is called terminates. The timeout is optional argument.
is_alive()It returns if process is alive.
terminate()As the name suggests, it is used to terminate the process. Always remember – the terminate() method is used in Linux, for Windows, we use TerminateProcess() method.
kill()This method is similar to the terminate() but using the SIGKILL signal on Unix.
close()This method is used to close the Process object and releases all resources associated with it.
qsize()It returns the approximate size of the queue.
empty()If queue is empty, it returns True.
full()It returns True, if queue is full.
get_await()This method is equivalent get(False).
get()This method is used to get elements from the queue. It removes and returns an element from queue.
put()This method is used to insert an element into the queue.
cpu_count()It returns the number of working CPU within the system.
current_process()It returns the Process object corresponding to the current process.
parent_process()It returns the parent Process object corresponding to the current process.
task_done()This function is used indicate that an enqueued task is completed.
join_thread()This method is used to join the background thread

Python multiprocessing join

The join method blocks the execution of the main process until the process whose join method is called terminates. Without the join method, the main process won’t wait until the process gets terminated.joining.py


from multiprocessing import Process
import time

def fun():

    print('starting fun')
    print('finishing fun')

def main():

    p = Process(target=fun)

if __name__ == '__main__':

    print('starting main')
    print('finishing main')

The example calls the join on the newly created process.

$ ./joining.py
starting main
starting fun
finishing fun
finishing main

The finishing main message is printed after the child process has finished.

$ ./joining.py
starting main
finishing main
starting fun
finishing fun

When we comment out the join method, the main process finishes before the child process. It is important to call the join methods after the start methods.join_order.py


from multiprocessing import Process
import time

def fun(val):

    print(f'starting fun with {val} s')
    print(f'finishing fun with {val} s')

def main():

    p1 = Process(target=fun, args=(3, ))
    # p1.join()

    p2 = Process(target=fun, args=(2, ))
    # p2.join()

    p3 = Process(target=fun, args=(1, ))
    # p3.join()


    print('finished main')

if __name__ == '__main__':


If we call the join methods incorrectly, then we in fact run the processes sequentially. (The incorrect way is commented out.)

Python multiprocessing is_alive

The is_alive method determines if the process is running.alive.py


from multiprocessing import Process
import time

def fun():

    print('calling fun')

def main():

    print('main fun')

    p = Process(target=fun)

    print(f'Process p is alive: {p.is_alive()}')

if __name__ == '__main__':

When we wait for the child process to finish with the join method, the process is already dead when we check it. If we comment out the join, the process is still alive.

Python multiprocessing Process Id

The os.getpid returns the current process Id, while the os.getppid returns the parent’s process Id.process_id.py


from multiprocessing import Process
import os

def fun():


    print('calling fun')
    print('parent process id:', os.getppid())
    print('process id:', os.getpid())

def main():

    print('main fun')
    print('process id:', os.getpid())

    p1 = Process(target=fun)

    p2 = Process(target=fun)

if __name__ == '__main__':

The parent Id is the same, the process Ids are different for each child process.

Differentiation of Python Multiprocessing
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