Created Readme

README.md

@@ -1 +1,112 @@
-# Multithreading
+# 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
+
+
+ ![table](https://github.com/prishasawhney/Multithreading/blob/main/Table.png)
+
+
+- Plotting the results
+
+
+![graph](https://github.com/prishasawhney/Multithreading/blob/main/Graph.png)
+
+
+- CPU Usage
+
+
+![cpu usage](https://github.com/prishasawhney/Multithreading/blob/1e1d2e05ddadbd42cf3ab72906dab6ed78b6ea84/CPU%20Usage.png)
+
+
+## Author
+
+- [Prisha Sawhney](https://github.com/prishasawhney)