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XLang™

XLang™ is a high-performance glue language designed to seamlessly integrate system components and application subsystems. It also serves as an excellent embedding language that can be easily integrated with application and game systems, thanks to its lightweight stack, complete syntax, and robust runtime for application integration. Moreover, XLang™ features a syntax that is similar to Python, making it familiar and easy to adopt for developers.

Crafted specifically for AI and IoT applications, XLang™ delivers dynamic, high-performance computing by leveraging advanced features such as optimized tensor expressions with partial optimization. This approach enables efficient neural network construction and data-intensive computing, making XLang™ an ideal choice for distributed environments and deep learning tasks.

Key Features

• High Efficiency:
  XLang™ exhibits a significant performance advantage over Python, delivering much faster execution times in AI and deep learning applications.
• High-Performance Glue Capabilities:
  Seamlessly integrate system components and application subsystems with fast method calls (efficient inter-process communication even with large data payloads), a robust event and notification system for handling high-volume data exchanges, and enhanced performance through direct AST execution and parallel expression-based data flows.
• Effortless API Exposure:
  Expose APIs within developed systems or applications without the complexity of traditional methods (e.g., Python C extensions). XLang™ allows direct, in-site API exposure for rapid integration.
• Native Thread Safety and Asynchronous Programming:
  Designed from the ground up to be thread safe, XLang™ includes built-in support for threads, asynchronous operations, and natural task management, making it simple to write concurrent code and handle large-scale data exchanges.
• Optimized Tensor Computing:
  A fully optimized tensor computing architecture enables effortless neural network construction via tensor expressions.
• GPU Performance Boost:
  In CUDA-enabled GPU environments, inference and training performance can be enhanced by 6 to 10 times thanks to automated tensor data flow graph generation and target-specific compilation.

Contributing

If you're interested in contributing to the XLang™ project, we would love to hear from you. Whether you're a developer, tester, or simply passionate about advancing this technology, please reach out. For more information or to get involved, send us an email at [email protected] and we'll provide the necessary details.

XLang™ has been thoroughly tested on Linux and Windows platforms—including successful deployments on Raspberry Pi boards and the Raspberry Pi Pico. If you need specific build instructions or have any questions regarding the Raspberry Pi Pico setup, please contact us at the same email address. We're here to support you and ensure your XLang™ experience is smooth and rewarding.

Building XLang™

For optimal performance, please ensure that XLang™ is built in Release mode. You can do this by running:

cmake -DCMAKE_BUILD_TYPE=Release ..

Windows

1. Clone the repository:

   git clone https://github.com/xlang-foundation/xlang.git

2. Open the XLang™ folder in Visual Studio.
3. Select your configuration (e.g., Local Machine/x64-Debug, WSL:Ubuntu/WSL-GCC-Debug).
4. Build via Visual Studio's build menu.

Linux (Ubuntu)

1. Install prerequisites:
   • UUID (required):

     sudo apt-get install uuid-dev

   • OpenSSL (for HTTP plugin):

     sudo apt-get install libssl-dev
     sudo apt-get install libcurl4-openssl-dev

   • Python3 (optional for Python library integration):

     sudo apt-get install python3-dev
     pip install numpy

     (To disable Python integration, comment out add_subdirectory("PyEng") in CMakeLists.txt.)
2. Clone the repository:

   git clone https://github.com/xlang-foundation/xlang.git

3. Navigate to the cloned directory:

   cd xlang

4. Create and enter the build directory:

   mkdir out && cd out

5. Generate build files:

   cmake ..

6. Compile:

   make

macOS

1. Install prerequisites:
   • UUID (required):

     brew install ossp-uuid

   • OpenSSL (for HTTP plugin):

     brew install openssl
     brew install curl

   • Turbo-jpeg (optional for image module):

     brew install jpeg-turbo

   • Python3 (optional for Python library integration):

     brew install python3
     pip install numpy

     (To disable Python integration, comment out add_subdirectory("PyEng") in CMakeLists.txt.)
2. Clone the repository:

   git clone https://github.com/xlang-foundation/xlang.git

3. Navigate to the cloned directory:

   cd xlang

4. Create and enter the build directory:

   mkdir out && cd out

5. Generate build files:

   cmake ..

6. Compile:

   make

You can also use Xcode to open the XLang™ folder for compilation.

Building for Android

1. On Windows, install Android Studio.
2. Open the XLang™ project from the xlang\Android folder.
3. Build using Android Studio's Build menu.

Running XLang™

Navigate to the XLang™ executable folder and run the xlang command:

$ ./xlang
xlang [-dbg] [-enable_python|-python]
      [-run_as_backend|-backend] [-event_loop]
      [-c "code,use \n as line separator"]
      [-cli]
      [file parameters]
xlang -help | -? | -h for help

Example Commands

• Running a Script File:
  To run an XLang™ script file:

  $ ./xlang your_script.x

• Running Inline Code with Event Loop:
  To execute inline code:

  $ ./xlang -c "print('Hello, XLang!')"

• Running in Command-Line Interface (CLI) Mode:
  To start in CLI mode without executing a file:

  $ ./xlang -cli

Under the test folder, you'll find numerous XLang and Python code examples for testing. While some files may currently break, we are actively working to improve compatibility with Python syntax and its ecosystem.

Debugging in VS Code

• Install the XLang™ plugin.
• Start XLang™ with:

  -event_loop -dbg -enable_python

• You can launch a new XLang™ instance or attach to an existing application/process that uses XLang™ embedding.
• Open or create a .x file and begin debugging from the VS Code menu.

Note: Debugging in VS Code has not been tested on macOS.

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Happy coding with XLang™!
For any questions or support, please reach out at [email protected].