Multithreading In Python - Introduction

Multi Threading Overview:

Every modern operating system supports Threads. When threads where introduced in earlier days most of the machines(not including mainframes and other mini computers) had a single processor. Popular word-processors, browsers and any other GUI based program will not prevent the user interaction when such programs had to work behind the scenes on several tasks. Example, a word-processor receiving input from user at the same time writing several blocks of data onto the harddisk. In this example, input from the user will be managed by a GUI thread and writing to the disk will be handled by a writer thread.

Multithreading on UniProcessor Systems:

Remember, the CUP is just single CPU. Was the single CPU capable of executing more than one instruction or a thread at that time?
No. Those CPUs were not multicore ones they were capable of executing just one instruction at a time.
Then how multiple threads were executed in such single CPU systems? To continue further we need to define what is a thread.
A thread is a set of instructions that forms a control flow within the same program/process.
When a CPU governed by a modern operating system executes a process it does not execute all the instructions as a single 
stream. Instead, it executes instructions from multiple threads. When the current instruction  under execution
belongs to Thread A, all other threads are not in CPU. They are under the status "Under Execution". They are in some other
status. They might be in wait state, they might be in ready state and so on.
Be aware of the fact that on a single CPU with single core only one thread can be executed at one point in time.
The time duration in which the instruction(s) belonging to a thread are inside the CPU under execution status is called
time slice. Operating systems make time slices and assign to these threads based on their priority so that these threads 
get more time or less time.

So it is very clear from above that the threads while executed under a Single Processor System, create an illusion to the
user multiple activities within a process are executed at the same time. Yes, it is just an illusion that these threads
run parallel in a  Single Processor System. In reality they are executed one by one in a time sliced manner.

Multithreading on MultiProcessor Systems:

With the advent of multi core systems CPUs come with multiple cores with each one of the core being capable of executing
one thread at a time. When multiple threads are run in multiple cores these threads are executed in parallel at the same
time. With multi core systems true parallelism is a reality, not an illusion.

Now a quick summary of multithreading: A thread is a control of execution, sequence of instructions with in a process.
A process is a program that runs on the operating system. 
Execution of a process moves forward as multiple threads belonging to different processes are executed inside the CPU.

Multithreading in Python:

A thread in a Python program is represented by the Thread Class.
In a Python program a thread can be constructed in any of the two ways below:
    1. By passing a callable object inside the thread constructor
    2. By overriding the run() method of the thread class
    

1. Create a Python thread with a callable object:


When creating the thread with a callable object, the function that forms the callable object, becomes the
thread body. Whatever the python statements given inside that function forms a thread.

Once an object is created using a callable object the thread can be started by calling the start() method
on the thread object.

e.g., 
PrimeNumberThread = Thread(target=ProducePrimeNumbers)
PrimeNumberThread.start()

In the above example ProducePrimeNumbers is a python function i.e., a callable object passed as a target
parameter of the Thread constructor.

Example: A Multithread Python program to print prime numbers

 

from threading import Thread

 

#Callable function forming the thread body for the Prime Number Producer

def ProducePrimeNumbers():

    print("Prime number thread started")

    #2 is a prime number

    print(2)

 

    for PrimeNumberCandidate in range (2,20):

       

        IsPrime = False

       

        for divisor in range(2,PrimeNumberCandidate):

            #Skip the Composite Number

            if PrimeNumberCandidate%divisor == 0:

                IsPrime = False;

                break

            else:

            #Mark the Prime Number

                IsPrime = True;

       

        #Print the Prime Number

        if IsPrime == True:

            print(PrimeNumberCandidate) 

           

    print("Prime number thread exiting")                     

 

print("Main thread started")   

PrimeNumberThread = Thread(target=ProducePrimeNumbers)

 

#Start the prime number thread

PrimeNumberThread.start()

 

#Let the Main thread wait for the prime thread to complete

PrimeNumberThread.join()

print("Main thread resumed")

print("Main thread exiting")

 

 

2. Create a Python thread by overriding the run() method:

from threading import Thread

 

# A thread class to produce Fibonacci Numbers

class FibonacciThread(Thread):

    def __init__(self):

        Thread.__init__(self)

       

    #override the run method to define thread body   

    def run(self):

        print("Fibonacci Thread Started")

        firstTerm   = 0

        secondTerm  = 1

        nextTerm    = 0

 

        for i in range(0,20):

            if ( i <= 1 ):

                nextTerm = i

            else:

                nextTerm    = firstTerm + secondTerm

                firstTerm   = secondTerm

                secondTerm  = nextTerm

 

            print(nextTerm)

 

        print("Fibonacci Thread Ending")           

 

print("Main Thread Started")

MyFibonacciThread =  FibonacciThread()    

MyFibonacciThread.start()

 

print("Main Thread Started to wait for the Fibonacci Thread to complete");

MyFibonacciThread.join()

print("Main Thread Resumed");

print("Main Thread Ending");

 

Python Thread Methods

Thread Constructor

start()

run()

join

is_alive()

daemon


Copyright 2019 © pythontic.com