README.md

MultiThreadingPython

Prisha Sawhney

102116052

3CS10

This is a multithreading program written in Python that demonstrates parallel execution using threads.

Overview

The program performs matrix multiplication using multiple threads to demonstrate the parallel processing capabilities of Python's threading module.

Requirements

To run this program, you need:

• Python 3.x installed on your system
• Basic understanding of multithreading concepts in Python

Usage

1. Clone this repository to your local machine
2. Navigate to the repository directory
3. Run the program using any notebook editor

Methodology

• Import necessary libraries

import time
import threading
import random as r
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

• Create a constant matrix of the order 1000*1000

matrix1 = np.eye(1000)

• Created the task function that generates a random matrix and multiplies it with the constant matrix

def task(matrix1):
    matrix2 = np.random.rand(1000, 1000)
    result = np.dot(matrix1, matrix2)
    return

• Created a parametrized function for threading

def thread_task(num_of_threads):
    startTime = time.time()
    activeThreads = threading.active_count()
    
    print("Program Started for", num_of_threads, "threads")
    
    # Multiplying 100 matrices
    for i in range(100):
        t=threading.Thread(target=task, args=(matrix1,))
        t.start()
        while True:
            if threading.active_count() - activeThreads + 1 <= num_of_threads:
                break
            time.sleep(1)
    
    # Wait for all the threads to finish
    while True:
        if threading.active_count() == activeThreads:
            break
        else:
            print("Active Threads: ", threading.active_count())
            print("Threads left:", threading.active_count() - activeThreads)
            time.sleep(1)
            

    print("Program Finished for", num_of_threads, "threads")
    print("Time taken: ", time.time()-startTime)
    return time.time()-startTime

• Create a dataframe to store results

result = pd.DataFrame(columns=['Threads', 'Time (Sec)'])

• Calling the function and storing the results for different number of threads

for threads in range(1,11):
    new_row = pd.DataFrame({'Threads': [int(threads)], 'Time (Sec)': [thread_task(threads)]})
    result = pd.concat([result, new_row], ignore_index=True)
    print("\n")

• Displaying the results

• Plotting the results

• CPU Usage

Author

• Prisha Sawhney