Process Pools in Python

In concurrent programming, where multiple tasks need to be completed simultaneously, multiprocessing plays a vital role in achieving high-performance computing. And one of the ways of implementing multiprocessing in Python is through the use of Process Pools. A Process Pool is a group of worker processes that are created to handle a pool of tasks. The use of Process Pools can significantly speed up the processing time of tasks that would otherwise take a considerable amount of time when executed sequentially. In this tutorial, we will learn about implementing Process Pools in Python and how to use them to handle intensive computational tasks more efficiently. So, let's dive in!

ProcessPoolExecutor class in Python :

• The  ProcessPoolExecutor  class is a part of the  concurrent.futures  module in Python.
• It provides a simple way to create and manage a pool of worker processes in Python.
•  ProcessPoolExecutor  can be useful for executing CPU intensive tasks in parallel to speed up the execution time.
• Here's an example of how to use the  ProcessPoolExecutor  class to execute CPU bound tasks in parallel.
• Code Sample : 

import concurrent.futures

def square(num):
   return num * num

if __name__ == '__main__':
   with concurrent.futures.ProcessPoolExecutor() as executor:
       numbers = [1, 2, 3, 4, 5]
       results = executor.map(square, numbers)
   
   for result in results:
       print(result)
       
       
       

How to use the ProcessPoolExecutor Class :

• To use the  ProcessPoolExecutor  class, we need to follow these steps:
  • Import the  concurrent.futures  module
  • Create an instance of the  ProcessPoolExecutor  class
  • Use the  submit  method to submit tasks to the pool of workers
  • Use the  map  method to execute the tasks and retrieve the results
• Code Sample : 

import concurrent.futures

def square(num):
   return num * num

if __name__ == '__main__':
   with concurrent.futures.ProcessPoolExecutor() as executor:
       # submit tasks to the pool of workers
       futures = [executor.submit(square, num) for num in range(1, 6)]
       
       # execute the tasks and retrieve the results
       for future in concurrent.futures.as_completed(futures):
           print(future.result())
           
           
           

• In the above example, the submit method is used to submit tasks to the pool of workers.
• The as_completed method is used to execute the tasks and retrieve the results as they become available.