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

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.

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

1. CUDA Toolkit 11.0+

   • Download fromNVIDIA CUDA Downloads
   • Follow installation instructions for your platform
   • Verify installation withnvcc --version

2. CMake 3.18+

   • Download fromCMake Website
   • Add to system PATH during installation

3. Visual Studio 2022 (Windows) orGCC 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

1. Open the project folder in VS Code

2. VS Code should automatically detect the CMake configuration if not then pressCtrl+Shift+P and selectCmake:ConfigureConfigure 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:

   

# Create build directorymkdir build&&cd build# Configure with CMakecmake ..# Buildcmake --build.

# From build directory./Debug/order_book.exe

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

Example output:

# From build directory./Debug/parallel_sort.exe

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

Example output:

# From build directory./Debug/zero_copy_processor.exe

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

Example output:

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

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

├── 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

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