Introduction to OpenMP

This repository contains a Jupyter notebook that provides an introduction to using OpenMP for parallelization in Python. The notebook demonstrates how to implement multiprocessing using OpenMP, along with the necessary concepts and examples.

Repository Contents

Introduction_to_OpenMP.ipynb: The main Jupyter notebook that covers the basics of OpenMP, including setup, usage, and examples.

Learning Objectives

At the end of this experiment, you will be able to:

Understand parallelization in Python
Implement multiprocessing using OpenMP

Information

OpenMP is an Application Program Interface (API), jointly defined by a group of major computer hardware and software vendors. It provides a portable, scalable model for developers of shared memory parallel applications.

Key Points

OpenMP: The easiest to use with minimal learning overhead, most key parallel design patterns can be learned with OpenMP.
Concurrency: A condition of a system in which multiple tasks are logically active at one time.
Parallelism: A condition of a system in which multiple tasks are actually active at one time.

Getting Started

To get started with the notebook, follow these steps:

Prerequisites

Ensure you have the following installed on your system:

Python 3.x
Jupyter Notebook or JupyterLab
Necessary Python packages (listed in the requirements.txt file)

Installation

Clone the repository to your local machine:

git clone https://github.com/Praveen76/Introduction-to-OpenMP.git
cd Introduction-to-OpenMP

Install the required Python packages:
```
pip install -r requirements.txt
```

Usage

Launch Jupyter Notebook or JupyterLab:
```
jupyter notebook
```
or
```
jupyter lab
```
Open the notebook Introduction_to_OpenMP.ipynb.
Follow the instructions and run the cells in the notebook to learn about and use OpenMP for parallelization in Python.

Notes

The notebook contains various examples and use cases for OpenMP. Modify the examples as needed to fit your specific requirements.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an issue to improve this repository.

Issues:

If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.

Contact:

The code has been tested on Windows system. It should work well on other distributions but has not yet been tested. In case of any issue with installation or otherwise, please contact me on Linkedin

Happy coding!!

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.