El uso de la programación concurrente en Python es interesante y sorprendente. Experimentando con Carnets y Google Colaboratory he llegado a la conclusión que su uso se justifica según el problema a resolver.

En Carnet el uso de multiprocessing no esta soportado hasta ahora. Su creador Nicolas Holzschuch comenta lo siguiente:

The short answer is:"you can't use multiprocessing with Carnets". I'm sorry about that, but it is really too difficult to support.> >The long answer is:multiprocessing in Python relies on multiple calls to fork(), with a pool of process and a queue for tasks to be executed. iOS does not have the fork() system call, so there is no way to make this work, and I don't think there is one.> Since most iPads have only 4 cores anyway (and some cores are already used by the system and the Jupyter server), I am not certain we would gain much processing power anyway.

En las siguientes celdas puedes observar y experimentar como se comporta el tiempo de ejecución en Jupyter usando Threading y Multiprocessing

%%writefile fibonacci.py
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

from concurrent.futures import ThreadPoolExecutor
from fibonacci import fibonacci

def fibs(n):
    """Fibonacci sequentially"""
    for i in range(1, n + 1):
        _ = fibonacci(i)

def fibs_thread(n):
    """Fibonacci with Threads"""
    with ThreadPoolExecutor(max_workers=4) as exct:
        exct.map(fibonacci, range(1, n + 1))

if __name__ == "__main__":
    N=30
    %timeit fibs(N)
    print()
    %timeit fibs_thread(N)

from multiprocessing import Pool, cpu_count
from fibonacci import fibonacci

def fibs_multiprocess(n):
    """Fibonacci with CPU Multiprocess"""
    with Pool(processes=cpu_count()) as cpu_pool:
        cpu_pool.map(fibonacci, range(1, n + 1))

if __name__ == "__main__":
    N=30
    %timeit fibs_multiprocess(N)

Referencias