Solved: multiprocessing map

Multiprocessing is a popular technique in Python programming that allows you to run multiple processes concurrently, often resulting in performance improvements and more efficient use of system resources. This article dives into the use of the multiprocessing library in Python, specifically focusing on the map function. The map function lets you apply a function to each item in an iterable, such as a list, and return a new list with the results. By leveraging multiprocessing, we can parallelize this process for greater efficiency and scalability.

In this article, we will explore the problem for which multiprocessing with map function can be an excellent solution, discuss the relevant libraries and functions, provide a step-by-step explanation of the code, and delve into related topics that build on the backbone of multiprocessing and the map function.

Multiprocessing Map: The Problem and Solution

The problem we aim to solve is to improve the performance and efficiency of applying a function to each item in a large iterable, such as a list, tuple, or any other object that supports iteration. When faced with such tasks, using the built-in map function or list comprehensions can be quite slow and inefficient.

The solution is to utilize the multiprocessing library in Python, specifically, the Pool class and its map method. By using the multiprocessing function, we can distribute the execution of our function across multiple processes.

Step-by-Step Explanation of the Code

Let’s break down the code and illustrate how to use the multiprocessing map function effectively:

import multiprocessing
import time

def square(n):
    return n * n

# Create the list of numbers
numbers = list(range(10))

# Initialize the multiprocessing Pool
pool = multiprocessing.Pool()

# Use the map function with multiple processes
squared_numbers =, numbers)

  1. First, import the multiprocessing module, which contains the tools necessary to utilize parallel processing in Python.
  2. Create a function called square that simply sleeps for half a second and then returns the square of its input argument. This function simulates a calculation that takes a reasonable amount of time to complete.
  3. Generate a list called numbers, which contains integers from 0 to 9 (inclusive).
  4. Initialize a Pool object from the multiprocessing module. The Pool object serves as a means to manage the worker processes that you will use to parallelize your tasks.
  5. Call the map method on the pool object, and pass in the square function and the numbers list. The map method then applies the square function to each item in the numbers list concurrently, using the available worker processes in the pool.
  6. Print the resulting list of squared_numbers, which should contain the squared values from the numbers list.

Python Multiprocessing Library

The Python multiprocessing library provides an intuitive means of implementing parallelism in your program. It masks some of the complexity typically associated with parallel programming by offering high-level abstractions like Pool. The Pool class simplifies the distribution of work across multiple processes, enabling the user to experience the benefits of parallel processing with minimal hassle.

Python Itertools Module and Related Functions

While multiprocessing is an excellent solution for many parallel tasks, it’s worth mentioning that Python also provides other libraries and tools that cater to similar needs. The itertools module, for instance, offers a wealth of functions that operate on iterables, often with improved efficiency. Some itertools functions like imap() and imap_unordered() can parallelize the process of applying a function to an iterable. However, it’s important to note that itertools focuses primarily on iterator-based solutions, whereas the multiprocessing library offers a more comprehensive approach to parallelism, providing additional tools and capabilities beyond map-like functions.

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