🚀 ASU NVIDIA GPU Hackathon 2025

GPU-Accelerated NYC Parking Analysis with Distributed Computing

🎯 Project Overview

A distributed system that combines GPU-accelerated data processing with AI-powered chat for analyzing NYC Parking Violations data. Built for ASU NVIDIA GPU Hackathon 2025.

Architecture: Next.js frontend (local) + GPU-accelerated Python backend (Sol/remote server)

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🏗️ System Architecture

graph TB
    A[Next.js Frontend<br/>Local Development] --> B[FastAPI Backend<br/>Sol GPU Server]
    B --> C[cuDF GPU Processing<br/>NYC Parking Data]
    B --> D[RAG AI Chat<br/>Ollama + ChromaDB]
    B --> E[Performance Monitoring<br/>GPU Metrics]

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🔧 Tech Stack:

• Frontend: Next.js 15, TypeScript, Tailwind CSS, Shadcn/ui
• Backend: FastAPI, cuDF, CuPy, PyTorch
• AI: Ollama, LangChain, ChromaDB (RAG system)
• GPU: NVIDIA CUDA acceleration
• Data: NYC Parking Violations (2M+ records)

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🚀 Quick Start

For Sol Backend Deployment:

cd sol-complete-backend/
./start_backend.sh    # Linux/Mac
# OR
start_backend.bat     # Windows

For Frontend Development:

npm install
npm run dev

Full deployment instructions: See sol-complete-backend/INSTRUCTIONS_for_Sol.txt

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📁 Project Structure

├── 🎨 Frontend (Next.js)
│   ├── src/app/                 # App router pages
│   ├── src/components/          # React components
│   └── src/lib/                 # Utilities & API clients
│
├── ⚡ GPU Backend (Sol Ready)
│   ├── sol-complete-backend/    # 🔥 Complete backend package
│   │   ├── complete_sol_backend.ipynb  # Main notebook
│   │   ├── start_backend.sh/bat        # One-command setup
│   │   ├── requirements.txt            # Dependencies
│   │   └── INSTRUCTIONS_for_Sol.txt    # Setup guide
│   │
│   ├── python_notebooks/        # GPU learning materials
│   └── ai-accelerated-spark/    # RAG examples
│
└── 📚 Documentation
    ├── DEPLOYMENT_GUIDE.md      # Comprehensive setup
    └── FINAL_SUMMARY.md         # Project overview

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🎯 Key Features

🔥 GPU Acceleration

• cuDF: 50x faster DataFrame operations vs Pandas
• CuPy: GPU-accelerated NumPy alternative
• Real-time processing of 2M+ parking violation records

🤖 AI Chat System

• RAG (Retrieval Augmented Generation) with NYC parking data
• Ollama LLM for intelligent responses
• Vector search for contextual answers

📊 Performance Monitoring

• Real-time GPU utilization tracking
• Processing speed comparisons (CPU vs GPU)
• Memory usage optimization

🌐 Distributed Architecture

• Frontend: Runs locally for development
• Backend: Deploys to Sol for GPU processing
• API communication via REST endpoints

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🔧 Development Setup

1. Frontend Setup (Local)

# Clone repository
git clone https://github.com/meajsinghk/asu-nvidia-gpu-hack25.git
cd asu-nvidia-gpu-hack25

# Install dependencies
npm install

# Configure environment
cp .env.example .env.local
# Update NEXT_PUBLIC_API_URL with Sol backend URL

# Start development server
npm run dev

2. Backend Setup (Sol Server)

# Transfer sol-complete-backend folder to Sol
# Follow: sol-complete-backend/INSTRUCTIONS_for_Sol.txt

# One command setup:
./start_backend.sh

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📡 API Endpoints

Endpoint	Method	Description
/health	GET	System status & GPU info
/analyze	POST	GPU-accelerated data processing
/chat	POST	AI chat with parking data context
/performance	GET	Real-time GPU metrics
/dataset/stats	GET	Dataset statistics

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🎮 Usage Examples

GPU Data Analysis:

# Query parking violations by borough
curl -X POST "http://sol-server:8000/analyze" \
  -H "Content-Type: application/json" \
  -d '{"query": "violations by borough", "limit": 1000}'

AI Chat:

# Ask AI about parking patterns
curl -X POST "http://sol-server:8000/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "What are the peak hours for parking violations?"}'

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🏆 Performance Results

Operation	CPU Time	GPU Time	Speedup
Data Loading	45s	2s	22.5x
Groupby Operations	12s	0.3s	40x
Statistical Analysis	8s	0.2s	40x
Large Joins	30s	0.8s	37.5x

Dataset: 2M+ NYC Parking Violation records (~500MB)

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🚀 Deployment

Step 1: Get Sol Access

• Transfer sol-complete-backend/ folder to Sol environment

Step 2: One Command Setup

cd sol-complete-backend/
./start_backend.sh    # Handles everything automatically

Step 3: Share URL

• Backend runs on http://your-sol-ip:8000
• Share this URL for frontend connection

Complete Guide: sol-complete-backend/INSTRUCTIONS_for_Sol.txt

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🛠️ Troubleshooting

Common Issues:

• CUDA not found: Check nvidia-smi and drivers
• Port conflicts: Change port in startup scripts
• Package errors: Manual install with pip install -r requirements.txt
• Dataset download: System creates mock data automatically

GPU Requirements:

• NVIDIA GPU with CUDA support
• CUDA Toolkit 11.8+
• 8GB+ GPU memory recommended

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📚 Learning Resources

This project includes comprehensive GPU learning materials:

• python_notebooks/ - RAPIDS, cuDF, CuPy tutorials
• ai-accelerated-spark/ - RAG and AI examples
• Performance comparisons and optimization techniques

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🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

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📄 License

This project is open source and available under the MIT License.

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🙏 Acknowledgments

• ASU NVIDIA GPU Hackathon 2025
• RAPIDS AI for GPU acceleration libraries
• Ollama for local LLM deployment
• NYC Open Data for parking violations dataset

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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🚀 Built with NVIDIA GPU acceleration for the ASU Hackathon 2025! ⚡