Utilizing The Multi Processing Tool With Numpy Python

I am trying to utilize multi processing tool with numpy arrays. I want to run func1() and func2() simultaneously and then use np.concatenate() and place Solution_1 and Solution_2 t

Solution 1:

For example:

import multiprocessing as mp
import numpy as np
num_cores = mp.cpu_count()

Numbers = np.array([1,2,3,4,5,6,7,8,9,10,11,12])
def func1():
     Solution_1 = Numbers + 10return Solution_1
def func2():
     Solution_2 = Numbers * 10return Solution_2

# Getting ready my cores, I left one aside
pool = mp.Pool(num_cores-1)
# This is to use all functions easily
functions = [func1, func2]
# This is to store the results
solutions = []
forfunctioninfunctions:
    solutions.append(pool.apply(function, ()))

solutions looks like:

[array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]),
 array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120])]

You could place that directly in a dataframe:

solutions = pd.DataFrame(solutions)
print(solutions)
    0   1   2   3   4   5   6   7   8    9   10   11
0  11  12  13  14  15  16  17  18  19   20   21   22
1  10  20  30  40  50  60  70  80  90  100  110  120

I see in a similar cases / alternatives:

• Global Variables, this is very similar to the original case post here: https://stackoverflow.com/a/40399562/7127519
• apply_async: https://stackoverflow.com/a/48933782/7127519
• A shared variable to communicate: How can I recover the return value of a function passed to multiprocessing.Process?
• multiprocessing.Manager(): https://stackoverflow.com/a/10415215/7127519

And others.