Hybrid_GPU-Accelerated_Huffman_Encoding

This project demonstrates a hybrid implementation of Huffman Encoding, combining GPU-accelerated frequency analysis using CuPy with traditional CPU-based Huffman tree construction and encoding. It runs smoothly on Google Colab with GPU support.

Click here to open in Colab

---

📌 Features

• ✅ Frequency counting on GPU using CuPy for high-speed analysis
• ✅ Huffman tree construction on CPU
• ✅ Compression of large strings with customizable input
• ✅ Fully executable on Google Colab (no local GPU required)

---

🔗 Run in Google Colab

👉 Click here to open in Colab

---

📦 Dependencies

• Python 3.x
• CuPy (compatible with CUDA 12.x on Colab)

Install CuPy in your Colab notebook with:

!pip install cupy-cuda12x

🧠 How It Works

1. Input a large text/string to be compressed.
2. GPU acceleration is used for counting symbol frequencies.
3. Huffman tree is built on the CPU from the frequency table.
4. Data is encoded into a compressed bitstring using the generated Huffman codes.

📁 File Structure

.
├── LICENSE
├── README.md
├── file_structure
├── main.py
└── main_notebook.ipynb

1 directory, 5 files

📜 License

This project is licensed under the MIT License.