This project demonstrates the concept of parallelism in CPU-bound tasks by computing prime numbers within a large numeric range using three different execution models: sequential execution, multithreading, and multiprocessing. The primary objective is to analyze and compare the performance of each approach and understand how parallel computing can improve processing speed for computationally intensive workloads.

The program calculates prime numbers in a specified range and measures the execution time required for each method. By comparing the results, the project highlights the limitations of Python threading due to the Global Interpreter Lock (GIL) and shows how multiprocessing can provide better performance for CPU-heavy operations.

The implementation follows a modular structure where the prime-checking logic is separated from the execution models. The sequential, threading, and multiprocessing versions all use the same core algorithm to ensure fair comparison. The program records execution times, calculates performance speedup, and displays the results for analysis.

Key concepts demonstrated in this project include CPU-bound computation, task parallelism, performance benchmarking, and the practical impact of Python’s concurrency mechanisms.

Technologies used in this project include Python along with standard libraries such as time, threading, and multiprocessing.

This project was developed as part of a coursework assignment to explore parallel processing concepts and evaluate different approaches for improving computational performance in Python.