How to Run a Python Script on Multiple Cores

This tutorial demonstrates how you can leverage the full power of multicore processors by running Python scripts across multiple cores. By spreading the workload across multiple cores, your script can perform tasks more quickly and efficiently.

Step 1: Installing Necessary Libraries

Python comes with a variety of libraries that can simplify your coding experience. For running scripts across multiple cores, two libraries are especially helpful: The multiprocessing library and the np library for mathematical operations.

You can install these libraries with the pip package installer. If you haven’t already done so, you can download Pip from the official website. To install multiprocessing and numpy, run the following commands: pip install multiprocessing
pip install numpy

Step 2: Creating a Python Script

We will start with a simple Python script. The script simply squares the numbers inside a list.

import numpy as np

def square(n):

return np.square(n)

Step 3: Implementing Multiprocessing

Now, we implement multiprocessing using Python’s multiprocessing library. Below is an example of how the implementation works.

import multiprocessing as mp

if __name__ == "__main__":

pool = mp.Pool(processes = 4)

results = [pool.apply(square, args=(x,)) for x in range(1,10)]

print(results)

This code creates a pool of four processes and uses the apply function to pass the function and arguments to each process.

Note, the number of processes can be adjusted according to the number of cores your CPU has.

Step 4: Validate the Output

Once the script is successfully run, you should see the square of the numbers from 1 to 9 in the output as below:

[1, 4, 9, 16, 25, 36, 49, 64, 81]

Full Python Script

Here is the complete code used in this tutorial: