Plotting the pool map for multi processing Python

How can I run multiple processes pool where I process run1-3 asynchronously, with a multi processing tool in python. I am trying to pass the values (10,2,4),(55,6,8),(9,8,7) for run1,run2,run3 respectively?

import multiprocessing 
def Numbers(number,number2,divider):
   value = number * number2/divider
   return value
if __name__ == "__main__":

   with multiprocessing.Pool(3) as pool:               # 3 processes
        run1, run2, run3 = pool.map(Numbers, [(10,2,4),(55,6,8),(9,8,7)]) # map input & output

Answer

You just need to use method starmap instead of map, which, according to the documentation:

Like map() except that the elements of the iterable are expected to be iterables that are unpacked as arguments.

Hence an iterable of [(1,2), (3, 4)] results in [func(1,2), func(3,4)].

import multiprocessing
def Numbers(number,number2,divider):
   value = number * number2/divider
   return value
if __name__ == "__main__":

   with multiprocessing.Pool(3) as pool:               # 3 processes
        run1, run2, run3 = pool.starmap(Numbers, [(10,2,4),(55,6,8),(9,8,7)]) # map input & output
   print(run1, run2, run3)

Prints:

5.0 41.25 10.285714285714286

Note

This is the correct way of doing what you want to do, but you will not find that using multiprocessing for such a trivial worker function will improve performance; in fact, it will degrade performance due to the overhead in creating the pool and passing arguments and results to and from one address space to another.