Multi Processor

multi.py

from multiprocessing import Pool
from time import time



data = ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve"]
progression = [1,2,4,6,12]

def do(name):
    start = time()
    i = 0
    while time() - start <= 1:
        i += 1
    return i


if __name__ == "__main__":

    for threads in progression:
        print(f'threads: {threads}')
        for slices in progression:
            sliced = data[0:slices]
            print(f"sliced: {sliced}")
            start = time()
            with Pool(threads) as p:
                total = sum(p.map(do, sliced, 1))
                print(f'{total} counted in {time() - start} seconds')

With 6 threads on 6 logical cores. I get about 30,500,000 iterations per second.
With 12 threads on 6 logical cores I get 37,500,000
So with this specific example it's about 22% more efficient leveraging hyper-threading to split cores.