How to Parallelize a While loop in Python?

Parallelizing a while loop in Python involves distributing the iterations of a loop across multiple processing units such as the CPU cores or computing nodes to execute them concurrently. This can significantly reduce the overall execution time of the loop, especially for tasks that are CPU-bound or require heavy computation. In this article, we'll explore various approaches to parallelizing a while loop in Python using different libraries and techniques.

How to Parallelize a While loop in Python?

Below, are the methods to explain how to Parallelise a while loop in Python:

Parallelize a While loop Using concurrent.futures

In this example, the below code uses ThreadPoolExecutor to parallelize a loop in Python. The function GFGrepresents the task to be performed in each iteration. The Geek function defines the loop's range and submits tasks to the executor for parallel execution. Each task corresponds to an iteration of the loop, processing a specific range defined by the start and end parameters.

Processing range (0, 1)
Processing range (1, 2)
Processing range (2, 3)
Processing range (3, 4)
Processing range (4, 5)

Parallelize a While loop Using Multiprocessing

In this example, below code parallelizes a While loop in Python using the multiprocessing module. It defines a function parallel_while_loop() which creates separate processes for each iteration of the loop using the Process class. Each process executes the GFG() function with iteration parameters.