Multiprocessing Pool for Loop in Python

What will you learn?

In this tutorial, you will learn how to boost performance by executing a for loop concurrently using the multiprocessing pool in Python. By leveraging multiple processes, you can efficiently handle computationally intensive tasks or I/O-bound operations.

Introduction to the Problem and Solution

When faced with tasks that demand high computational power or involve waiting for I/O operations, employing multiple processes can be a game-changer. One effective way to achieve parallelism in Python is by utilizing a multiprocessing pool. By distributing the workload among several processes, we can run our for loop concurrently, thereby enhancing efficiency.

To implement this solution, we will harness the power of Python’s built-in multiprocessing module. By creating a pool of worker processes that work simultaneously, we can optimize the execution of our for loop.


import multiprocessing

# Define the function that each process will execute
def process_task(item):
    # Task logic here

if __name__ == "__main__":
    # Data over which the 'for' loop iterates
    data = [1, 2, 3, 4, 5]

    # Create a multiprocessing pool with a specified number of processes (workers)
    with multiprocessing.Pool(processes=3) as pool:
        # Map each item in data to a worker process, data)

# Visit us at []( for more Python tips and tutorials.

# Copyright PHD


  • Importing multiprocessing: Importing the necessary module to utilize multiprocessing capabilities.
  • Defining Task Function: Defining the task logic that each worker process within the pool will execute.
  • Main Block: Ensuring that our main code runs only if it’s not imported as a module.
  • Creating Data Sequence: Defining a sample dataset for iteration within our for loop.
  • Initializing Pool: Creating a pool of worker processes with three workers using multiprocessing.Pool.
  • Mapping Tasks: Distributing each item from our data list across available worker processes for parallel processing.
    How does multiprocessing differ from multithreading?

    Multithreading involves threads sharing resources within a single process, while multiprocessing creates separate independent processes.

    Can I share data between processes within a multiprocess environment?

    Yes! You can share data between processes using shared memory objects or communication methods like queues and pipes.

    Are there any limitations when using multiprocessing in Python?

    Certain functions are not pickleable in Python, imposing restrictions on what can be executed within separate processes.

    Is there an upper limit on how many processes can be spawned using Multiprocessing?

    The maximum number of concurrent subprocesses varies based on system resources and OS limitations but typically ranges from hundreds to thousands.

    How does differ from Pool.apply() method?

    While both methods apply functions to elements in iterable sequences concurrently, map distributes work across multiple items whereas apply handles them sequentially one at a time.


    By utilizing Python’s multiprocessing module and creating pools of worker processes, you can effectively parallelize tasks. This approach is ideal for scenarios where boosting performance through concurrency is essential without introducing complexities associated with threading operations.

    Leave a Comment