CUDA HFT Fundamentals

This project implements key high-frequency trading (HFT) components in CUDA to demonstrate GPU acceleration for financial applications.

Components

1. Order Book - A limit order book implementation with matching engine - Learn more
2. Parallel Sort - High-performance sorting algorithms for market data - Learn more
3. Zero-Copy Market Data Processing - Low-latency market data feed processing using zero-copy memory techniques - Learn more

Each component contains its own detailed README explaining the implementation details, algorithmic approaches, and performance characteristics.

Prerequisites

Hardware Requirements

• NVIDIA GPU with Compute Capability 5.0 or higher
• Recommended: 4GB+ VRAM

Software Requirements

1. CUDA Toolkit 11.0+

   • Download from NVIDIA CUDA Downloads
   • Follow installation instructions for your platform
   • Verify installation with nvcc --version

2. CMake 3.18+

   • Download from CMake Website
   • Add to system PATH during installation

3. Visual Studio 2022 (Windows) or GCC 7+ (Linux)

   • For Windows: Install Visual Studio with "Desktop development with C++" workload
   • For Linux: sudo apt-get install build-essential (Ubuntu/Debian)

4. Visual Studio Code (Optional but recommended)

   • Install the "C/C++" and "CMake Tools" extensions

Building the Project

Using Visual Studio Code

1. Open the project folder in VS Code

2. VS Code should automatically detect the CMake configuration if not then press Ctrl+Shift+P and select Cmake:Configure Configure the project using CMake:

   

3. Select your compiler kit when prompted:

   

4. After succesful configuration it will create some build files:

   

5. Build the project using F7 or the CMake Build button:

   

   

6. The executables will be created in the build/Debug directory:

   

Using Command Line

# Create build directory
mkdir build && cd build

# Configure with CMake
cmake ..

# Build
cmake --build .

Running the Applications

Order Book Benchmark

# From build directory
./Debug/order_book.exe

# If you have GPU limitation
./Debug/order_book.exe [num_orders]

Example output:

Parallel Sort Benchmark

# From build directory
./Debug/parallel_sort.exe 

# If you have GPU limitation
./Debug/parallel_sort.exe [data_size]

Example output:

Zero Copy Processing Benchmark

# From build directory
./Debug/zero_copy_processor.exe 

# If you have GPU limitation
./Debug/zero_copy_processor.exe [data_size]

Example output:

Performance Analysis

The benchmarks demonstrate significant performance improvements when using GPU acceleration:

1. Order Book:

   • CPU Implementation: ~380,000 orders/sec
   • GPU Implementation: Improved throughput for large order sets

2. Sorting Algorithms:

   • CPU Sort: Baseline performance
   • Thrust Sort: 2-3x faster than CPU
   • Bitonic Sort: Variable performance based on data size

Troubleshooting

Common CUDA Installation Issues

1. CUDA Not Found

   • Ensure CUDA is installed and in your PATH
   • Check environment variables: CUDA_PATH should be set
   • Restart your system after installation

2. Compilation Errors

   • Verify CUDA Toolkit and VS/GCC versions are compatible
   • Update graphics drivers to latest version

3. CMake Configuration Issues

   • If you see warnings about FindCUDA being removed, you can safely ignore them
   • Make sure your CMake version is 3.18 or higher

Project Structure

├── CMakeLists.txt             # Project configuration
├── order-book/                # Order book implementation
│   ├── main.cu                # Entry point
│   ├── order_book.cu          # Implementation
│   └── order_book.cuh         # Header
├── parallel-sort/             # Sorting implementations
│   ├── main.cu                # Entry point
│   ├── parallel_sort.cu       # Implementation
│   └── parallel_sort.cuh      # Header
└── screenshots/               # Documentation images

License

This project is licensed under the MIT License - see the LICENSE file for details.