Create a Python program to perform this task like multithreading and synchronization

Create a Python program to perform this task like multithreading and synchronization, to make this process run faster?

The correct answer and explanation is:

Sure! Below is a simple Python program that demonstrates multithreading with synchronization using the threading module to run tasks faster by executing multiple operations in parallel.

🔧 Problem Example:

Suppose we have a task that needs to increment a shared counter 100,000 times using multiple threads. We’ll use multithreading to speed it up and a lock to synchronize access to shared data to avoid race conditions.

✅ Python Code:

import threading

# Shared resource
counter = 0

# Lock for synchronization
lock = threading.Lock()

# Function to increment the counter
def increment():
    global counter
    for _ in range(100000):
        # Acquire lock before modifying the shared variable
        with lock:
            counter += 1

# Create two threads
thread1 = threading.Thread(target=increment)
thread2 = threading.Thread(target=increment)

# Start the threads
thread1.start()
thread2.start()

# Wait for both threads to finish
thread1.join()
thread2.join()

# Print final counter value
print("Final counter value:", counter)

🧠 Explanation (300 words):

Multithreading in Python allows you to run multiple threads (smaller units of a process) concurrently. This can make programs run faster by doing multiple things at once, especially when tasks involve waiting (like file or network operations). However, when threads share data, like the counter in this program, it can lead to race conditions — where threads read and write data at the same time, causing unexpected results.

To fix this, we use synchronization with a Lock. The with lock: statement ensures that only one thread at a time can execute the code block where the shared resource (counter) is updated. This is crucial for data integrity.

In the example above, both threads call the increment() function, which tries to add 1 to counter 100,000 times. Without the lock, the final result would likely be less than 200,000 due to overlapping writes. But with the lock, every increment is executed safely.

Multithreading improves performance when tasks can be broken down and run in parallel. For CPU-bound tasks, Python’s Global Interpreter Lock (GIL) can be a limiting factor — but for IO-bound tasks or when synchronization is used correctly, it brings significant speed improvements and efficient CPU utilization.

This example shows how multithreading and synchronization can make programs faster and safe in concurrent environments.