⚡ GPU Code Optimizer AI

An AI-powered code analysis tool that uses NVIDIA Nemotron models via Ollama to identify GPU optimization opportunities in your code. Built for the NVIDIA GTC 2026 Golden Ticket Developer Contest.

🎯 What It Does

GPU Code Optimizer AI analyzes Python, CUDA, and C++ code to identify performance bottlenecks and suggest specific optimizations:

• Memory Coalescing - Detect uncoalesced memory accesses
• Kernel Fusion - Identify opportunities to combine kernels
• Shared Memory - Suggest shared memory optimizations
• Tensor Core Utilization - Recommend tensor core operations
• GPU Occupancy - Analyze thread block configurations
• Memory Bandwidth - Optimize data transfer patterns
• Compute Intensity - Improve arithmetic intensity
• Parallelism - Find parallelization opportunities

🚀 Quick Start

Prerequisites

1. Install Ollama: https://ollama.ai
2. Install NVIDIA Nemotron model:

   ollama pull nemotron

3. Python 3.8+

Installation

1. Clone this repository:

   git clone https://github.com/HUM4NITY/gpu-code-optimizer-ai.git
   cd gpu-code-optimizer-ai

2. Install Python dependencies:

   pip install -r requirements.txt

Run the Application

Option 1: Simple Setup (Static Frontend)

python app.py

Then open your browser to: http://localhost:8000

Option 2: Modern UI (Next.js Frontend - Recommended!)

# Terminal 1 - Start Backend
python app.py

# Terminal 2 - Start Frontend
cd frontend
npm install
npm run dev

Then open your browser to: http://localhost:3000

The Next.js version includes:

• ✨ Polished UI with animations
• 📋 Built-in code examples
• 🎨 Modern design with shadcn/ui
• ⚡ Better performance

📖 Usage

1. Paste Your Code - Enter Python, CUDA, or C++ GPU code
2. Select Model - Choose NVIDIA Nemotron or other available models
3. Analyze - Click "Analyze & Optimize"
4. Get Results - Receive detailed optimization suggestions with:
   • Severity levels (Critical, High, Medium, Low)
   • Specific issues and solutions
   • Code examples
   • Estimated speedup predictions

Example Analysis

import torch

def inefficient_batch_process(data):
    results = []
    for item in data:
        item_gpu = item.cuda()
        result = model(item_gpu)
        results.append(result.cpu())
    return results

The AI will identify:

• ❌ CPU-GPU transfer overhead
• ❌ No batch processing
• ❌ Synchronous execution
• ✅ Suggested optimization: Batch processing with single GPU transfer

🏗️ Architecture

gpu-code-optimizer/
├── app.py                 # FastAPI backend
├── frontend/              # Next.js modern UI (recommended)
│   ├── app/              # Next.js pages
│   ├── components/       # React components
│   └── lib/              # API client & types
├── static/               # Simple HTML/CSS/JS UI
│   ├── index_v2.html    # Alternative web interface
│   ├── style_v2.css     # Styling
│   └── script_v2.js     # Frontend logic
├── requirements.txt      # Python dependencies
└── README.md            # Documentation

Tech Stack:

• Backend: FastAPI for high-performance async API
• AI Engine: Ollama with NVIDIA Nemotron models
• Frontend (Modern): Next.js 14, React, TypeScript, Tailwind CSS, shadcn/ui, Framer Motion
• Frontend (Simple): Vanilla JavaScript (no build step!)
• Analysis: Custom prompt engineering for GPU optimization

🎮 Features

Real-Time Analysis

• Instant feedback on code quality
• Interactive web interface
• Multiple model support

Comprehensive Detection

• Memory access patterns
• Kernel optimization opportunities
• Thread configuration issues
• Data transfer inefficiencies

Actionable Insights

• Specific code examples
• Estimated performance gains
• Severity-based prioritization
• Category-based grouping

Developer Friendly

• Simple setup (< 5 minutes)
• No GPU required to run the analyzer
• Works with existing code
• Export and share results

🧪 Example Code Samples

The application includes built-in examples:

1. Inefficient CUDA Kernel - Matrix multiplication with poor memory access
2. Python GPU Computing - PyTorch code with optimization opportunities
3. NumPy Vectorization - CPU code that could benefit from GPU acceleration

Click "📋 Load Example" in the UI to try them!

🔧 API Endpoints

POST /api/analyze

Analyze code for GPU optimizations

Request:

{
  "code": "your code here",
  "language": "python",
  "model": "nemotron"
}

Response:

{
  "optimizations": [...],
  "summary": "Analysis summary",
  "overall_score": 85,
  "model_used": "nemotron"
}

GET /api/models

List available Ollama models

GET /api/examples

Get example code snippets

GET /api/health

Health check endpoint

🎓 How It Works

1. Code Submission - User submits code through web interface
2. Prompt Engineering - System creates a detailed analysis prompt
3. AI Analysis - Nemotron model analyzes code for GPU patterns
4. Result Parsing - Structured JSON response with optimizations
5. Visualization - Results displayed with severity and categories

🏆 NVIDIA GTC Contest Submission

This project is designed for the NVIDIA GTC Golden Ticket Developer Contest:

Targets Multiple Challenges:

• ✅ Ollama Challenge: Built with Ollama and open models
• ✅ Bryan Catanzaro: Showcases NVIDIA Nemotron models
• ✅ Sabrina Koumoin: Demonstrates NVIDIA technology

Why This Project Stands Out:

1. Practical Value - Solves real GPU optimization challenges
2. NVIDIA Focus - Directly aligned with GPU performance
3. Nemotron Showcase - Highlights model capabilities
4. Open Source - Community-driven development
5. Production Ready - Clean architecture, good UX

🚀 Future Enhancements

• Multi-file project analysis
• Integration with VS Code extension
• Performance benchmarking tools
• CI/CD pipeline integration
• Support for more languages (Rust, Julia)
• Automatic code refactoring
• Historical analysis tracking

📝 License

MIT License - Feel free to use, modify, and distribute!

🤝 Contributing

Contributions welcome! This is an open-source project for the NVIDIA developer community.

📧 Contact

Built by a developer passionate about GPU computing and AI.

For NVIDIA GTC Contest:

• Tag: #NVIDIAGTC
• Models: NVIDIA Nemotron via Ollama
• Category: Developer Tools, GPU Optimization, AI

🙏 Acknowledgments

• NVIDIA for Nemotron models and GPU technology
• Ollama for making model deployment simple
• FastAPI for excellent Python web framework
• Open Source Community for inspiration

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⚡ Built with NVIDIA Nemotron | Powered by Ollama | For Developers, By Developers