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 GFG
represents 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.
from concurrent.futures import ThreadPoolExecutor
def GFG(start, end):
print(f"Processing range ({start}, {end})")
def Geek():
start = 0
end = 5
with ThreadPoolExecutor(max_workers=4) as executor:
for i in range(start, end):
executor.submit(GFG, i, i+1)
if __name__ == "__main__":
Geek()
Output
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.
from multiprocessing import Process
def GFG(start, end):
# Perform computation or task within the loop
print(f"Processing range ({start}, {end})")
def parallel_while_loop():
# Define the range of iterations for the loop
start = 0
end = 5
# Create a separate process for the each iteration
processes = []
for i in range(start, end):
process = Process(target=GFG, args=(i, i+1))
processes.append(process)
process.start()
# Wait for all processes to finish
for process in processes:
process.join()
if __name__ == "__main__":
parallel_while_loop()
Output
Processing range (0, 1) Processing range (1, 2) Processing range (2, 3) Processing range (4, 5) Processing range (3, 4)
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