From For-Loop to Supercomputer: A Hands-On Introduction to Parallel Computing with OpenMP and MPI

Most research code starts the same way: a for-loop over a dataset, processing one item at a time. This tutorial shows how to break that bottleneck — step by step, from a single CPU core all the way to a distributed HPC cluster.

Starting from a plain Python loop (SISD — Single Instruction, Single Data), we walk through Flynn's Taxonomy to build intuition about why parallelism works, then implement the same workload four different ways: Python multiprocessing, C++ (sequential), C++ with OpenMP, and C++ with MPI. Each version comes with live benchmarks so attendees can see the speedup for themselves.

By the end of the session, attendees will understand:

1. The difference between shared-memory (OpenMP) and distributed-memory (MPI) parallelism

2. When to reach for each tool and when not to

3. The key OpenMP pragma that can parallelise a loop in one line

4. How MPI coordinates work across multiple machines, and why it scales to thousands of nodes

All code is provided as ready-to-run scripts. No prior parallel programming experience is required, just basic familiarity with Python and/or C++. In this repository you will find an example for running a [Monte Carlo Pi Estimation](https://www.geeksforgeeks.org/dsa/estimating-value-pi-using-monte-carlo/) using 20,000,000 random points implemented in:

1. Baseline Sequential Python

2. C++

3. Multiprocess Python

4. OpenMP

5. MPI