Going multithreaded

Key 1: Using threads to process in parallel.

Let's see how threads can help us in improving performance. In Python, due to Global Interpreter Lock, only one thread runs at a given time. Also, context is switched as all of them are given a chance to run. Hence, this is load in addition to computation. Hence, CPU-intensive tasks should take the same or more time. IO tasks are not doing anything but waiting, so they will get the boost. In the following code segment, threaded_iotask and threaded_cputask are two functions that are executed using separate threads. The code is run for various values to get results. The process function invokes multiple threads for tasks and sums up the timings taken:

import time
from tasker import cputask, iotask
from random import randint
import threading,random,string

def threaded_iotask(i):
    i[1] = iotask(i[0])

def threaded_cputask(i):
    i[1] = cputask(i[0])

stats = {}

def process(rep, cases=()):
    stats.clear()
    inputs = [...