https://github.com/pinedah/escom_high-performance-computing
This repository contains materials, notes, and projects for the High-Performance Computing (HPC) course in the Data Science degree at IPN. It covers the design and implementation of parallel computing, distributed systems, and GRID architectures to build high-performance computing systems.
https://github.com/pinedah/escom_high-performance-computing
cloud-computing data-science distributed-systems escom high-performance-computing hpc ipn parallel-computing school
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This repository contains materials, notes, and projects for the High-Performance Computing (HPC) course in the Data Science degree at IPN. It covers the design and implementation of parallel computing, distributed systems, and GRID architectures to build high-performance computing systems.
- Host: GitHub
- URL: https://github.com/pinedah/escom_high-performance-computing
- Owner: Pinedah
- Created: 2025-02-17T18:46:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-05T23:54:16.000Z (10 months ago)
- Last Synced: 2025-06-06T00:24:52.228Z (10 months ago)
- Topics: cloud-computing, data-science, distributed-systems, escom, high-performance-computing, hpc, ipn, parallel-computing, school
- Language: Cuda
- Homepage:
- Size: 434 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# High-Performance Computing (HPC)
## 📌 Course Overview
This repository contains materials, notes, and projects related to the **High-Performance Computing (HPC)** course as part of the **Data Science degree at IPN**. The course focuses on designing and implementing high-performance computing systems using **parallel computing, distributed systems, and GRID architectures**.
## 📖 Topics Covered
1. **High-Performance Computing Architectures**
- Types of HPC architectures
- Parallel processing systems
- Distributed computing systems
- GRID computing architectures
2. **Parallel Computing**
- Multiprocessors and multicomputers
- Interconnection networks
- Parallel algorithms
- Parallel programming (HPF, OpenMP, PVM, MPI)
3. **Distributed Systems**
- System models and architectures
- Process communication and concurrency control
- Distributed transactions and replication
- Fault tolerance and high availability
4. **GRID Computing Architectures**
- GRID resources and virtual organizations
- GRID computing standards (OGSA, OGSI, OGSA-DAI)
- GRID software and tools
- Performance metrics and optimization
## 🔧 Prerequisites
To follow along with the course materials and projects, ensure you have:
- Basic knowledge of **computer architecture and parallel computing**
- Familiarity with **distributed systems and networking**
- Python and relevant HPC libraries installed (e.g., `mpi4py`, `CUDA`)
## 📂 Repository Structure
```
📦 HighPerformanceComputing
┣ 📂 datasets # Datasets, databases and resources
┣ 📂 period1 # Homework and exercises from Period 1
┣ 📂 period2 # Homework and exercises from Period 2
┣ 📂 period3 # Homework and exercises from Period 3
┣ 📂 projects # Final projects and case studies
┣ 📜 computoAltoDesempeno_LCD2020 # Course overview
┣ 📜 README.md # This file
```
## 📚 Recommended Books
- **"Distributed Systems: Principles and Paradigms"** – Tanenbaum & Van Steen
- **"Principles of Parallel Programming"** – Lin & Snyder
- **"Programming Massively Parallel Processors"** – Kirk & Hwu
- **"Grid Computing for Developers"** – Silva
## 💡 How to Contribute
Contributions are welcome! To collaborate:
1. **Fork** this repository
2. **Clone** it to your local machine
3. **Create** a new branch: `git checkout -b feature-name`
4. **Commit** your changes: `git commit -m 'Added new content'`
5. **Push** to your fork: `git push origin feature-name`
6. **Submit a Pull Request**
## 📬 Contact
If you have any questions or suggestions, feel free to reach out!
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🚀 **Happy coding and high-performance computing!**