Python_Multiprocessing_Pool_Usage.py

# Python multiprocessing - Process-based parallelism
# The following scripts are written to demonstrate multiprocessing (Process-based parallelism) 
# using Python.
# Multiprocessing is a Python package that supports spawning processes using an API similar to 
# the threading module. The multiprocessing package offers both local and remote concurrency, 
# effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. 
# Due to this, the multiprocessing module allows the programmer to fully leverage multiple 
# processors on a given machine. It runs on both Unix and Windows.
# The multiprocessing module also introduces APIs which do not have analogs in the threading module. 
# A prime example of this is the Pool object which offers a convenient means of parallelizing the 
# execution of a function across multiple input values, distributing the input data across processes 
# (data parallelism). 
# The following example demonstrates the use of a pool:
 
from multiprocessing import Pool
import time

def f(x):
    return x*x

if __name__ == '__main__':

    with Pool(processes=4) as pool:         # start 4 worker processes

        result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process

        print(result.get(timeout=1))        # prints "100" unless your computer is *very* slow

        print(pool.map(f, range(10)))       # prints "[0, 1, 4,..., 81]"

        it = pool.imap(f, range(10))

        print(next(it))                     # prints "0"
        print(next(it))                     # prints "1"
        print(it.next(timeout=1))           # prints "4" unless your computer is *very* slow

        result = pool.apply_async(time.sleep, (10,))

        print(result.get(timeout=1))        # raises multiprocessing.TimeoutError