multiprocessing vs multithreading python code example

Example 1: multiprocessing python

"""A very simple parallel code example to execute parallel functions in python"""
import multiprocessing
import numpy as np
def multiprocessing_func(x):
	"""Individually prints the squares y_i of the elements x_i of a vector x"""
	for x_i in x:
		y=x_i**2 
		print('The square of ',x_i,' is ',y)
def chunks(input, n):
    """Yields successive n-sized chunks of input"""
    for i in range(0, len(input), n):
        yield input[i:i + n]
if __name__=='__main__':
	n_proc=4 #Numer of available processors
	x=np.arange(100) #Input
	chunked_x=list(chunks(x, int(x.shape[0]/n_proc)+1)) #Splits input among n_proc chunks
	processes=[] #Initialize the parallel processes list
	for i in np.arange(0,n_proc):
		"""Execute the target function on the n_proc target processors using the splitted input""" 
		p = multiprocessing.Process(target=multiprocessing_func,args=(chunked_x[i],))
		processes.append(p)
		p.start()
	for process in processes:
		process.join()

Example 2: python threading vs multiprocessing

The Python threading module uses threads instead of processes.
Threads uniquely run in the same unique memory heap.
Whereas Processes run in separate memory heaps.
This makes sharing information harder with processes and object instances.
One problem arises because threads use the same memory heap,
multiple threads can write to the same location in the memory heap
which is why the global interpreter lock(GIL) in CPython was created
as a mutex to prevent it from happening.