Python - Run same function in parallel with different parameters - GeeksforGeeks (2024)

In this article, we will learn how to run the same function in parallel with different parameters. We can run the same function in parallel with different parameters using parallel processing.

The number of tasks performed by the program can be increased by parallel processing, which decreases the total processing time. These assist in addressing large-scale issues. Using the standard multiprocessing module, by creating child processes, we can effectively parallelize simple tasks. This module provides an easy-to-use interface and includes a set of task submission and synchronization handling utilities.

Approach:

• Wemay construct a process running independently by subclassing the multiprocessing process. We can initialize the resource by extending the __init_ method, and we can write the code for the subprocess by implementing the Process.run() method. We see how to construct a process in the code below, which prints the assigned id.
• We need to initialize our process object and invoke the Process.start() method in order to spawn the process. Here, Process.start() will create a new process and invoke a method called Process.run().
• The code after p.start() is executed immediately before process p completes the mission. You may use Process.join to wait for task completion().

Let’s understand this with some examples.

Example 1:

import multiprocessingimport time# Process classclass Process(multiprocessing.Process): def __init__(self, id): super(Process, self).__init__() self.id = id def run(self): time.sleep(1) print("I'm the process with id: {}".format(self.id))if __name__ == '__main__': p = Process(0) # Create a new process and invoke the # Process.run() method p.start() # Process.join() to wait for task completion. p.join() p = Process(1) p.start() p.join()

Output:

We can also run the same function in parallel with different parameters using the Pool class. For parallel mapping, We have to first initialize multiprocessing.Pool() object. The first argument is the number of workers; if not given, that number will be equal to the number of elements in the system.

Example 2:

Let see by an example. In this example, we will see how to pass a function that computes the square of a number. Using Pool.map() we can map the function to the list and passing the function and the list of inputs as arguments, as follows:

import multiprocessingimport time# square functiondef square(x): return x * xif __name__ == '__main__': # multiprocessing pool object pool = multiprocessing.Pool() # pool object with number of element pool = multiprocessing.Pool(processes=4) # input list inputs = [0, 1, 2, 3, 4] # map the function to the list and pass # function and input list as arguments outputs = pool.map(square, inputs) # Print input list print("Input: {}".format(inputs)) # Print output list print("Output: {}".format(outputs))

Output:

Example 3:

from multiprocessing import Pooldef print_range(range): # print range print('From {} to {}:'.format(range[0], range[1]))def run_parallel(): # list of ranges list_ranges = [[0, 10], [10, 20], [20, 30]] # pool object with number of elements in the list pool = Pool(processes=len(list_ranges)) # map the function to the list and pass  # function and list_ranges as arguments pool.map(print_range, list_ranges)# Driver codeif __name__ == '__main__': run_parallel()

Output:

Python – Run same function in parallel with different parameters – FAQs

How to call the same function with different parameters in Python?

To call the same function with different parameters, simply invoke the function multiple times with different arguments.

Example:

def greet(name):
 print(f"Hello, {name}!")
# Calling the same function with different parameters
greet("Alice")
greet("Bob")
greet("Charlie")

How to call the same function in parallel in Python?

To call the same function in parallel, use the concurrent.futures.ThreadPoolExecutor or ProcessPoolExecutor for multithreading or multiprocessing.

Example with ThreadPoolExecutor:

from concurrent.futures import ThreadPoolExecutor
def greet(name):
 print(f"Hello, {name}!")
names = ["Alice", "Bob", "Charlie"]
with ThreadPoolExecutor() as executor:
 executor.map(greet, names)

Example with ProcessPoolExecutor:

from concurrent.futures import ProcessPoolExecutor
def greet(name):
 print(f"Hello, {name}!")
names = ["Alice", "Bob", "Charlie"]
with ProcessPoolExecutor() as executor:
 executor.map(greet, names)

How to parallelize a function in Python?

To parallelize a function, use concurrent.futures or multiprocessing to run the function concurrently.

Example with concurrent.futures:

See Also

For-Loops in Python - Data Science Discovery

from concurrent.futures import ThreadPoolExecutor
def compute(x):
 return x * x
inputs = [1, 2, 3, 4, 5]
with ThreadPoolExecutor() as executor:
 results = list(executor.map(compute, inputs))
print(results)

Example with multiprocessing:

from multiprocessing import Pool
def compute(x):
 return x * x
inputs = [1, 2, 3, 4, 5]
with Pool() as pool:
 results = pool.map(compute, inputs)
print(results)

How to run multiple tasks in parallel in Python?

To run multiple tasks in parallel, use concurrent.futures.ThreadPoolExecutor or ProcessPoolExecutor.

Example with ThreadPoolExecutor:

from concurrent.futures import ThreadPoolExecutor
def task(number):
 print(f"Task {number}")
tasks = [1, 2, 3, 4, 5]
with ThreadPoolExecutor() as executor:
 executor.map(task, tasks)

Example with ProcessPoolExecutor:

from concurrent.futures import ProcessPoolExecutor
def task(number):
 print(f"Task {number}")
tasks = [1, 2, 3, 4, 5]
with ProcessPoolExecutor() as executor:
 executor.map(task, tasks)

How to run a Python script multiple times in parallel?

To run a Python script multiple times in parallel, use the subprocess module to start new processes.

Example:

import subprocess
scripts = ["script1.py", "script2.py", "script3.py"]
processes = [subprocess.Popen(["python", script]) for script in scripts]
for process in processes:
 process.wait()