PyC is driven by several key concepts:
- Compiler Design: Implements a full compiler pipeline to educate users on transforming high-level code into machine instructions.
- LLVM Infrastructure: Provides a robust backend for optimization and code generation, allowing PyC to focus on frontend and AI-specific features.
- GPU Acceleration: Explores using GPUs for compiler tasks (e.g., tokenization), pushing traditional compiler boundaries.
- Modularity and Extensibility: Designed with independent components for easy feature addition and experimentation.
- AI Optimization: Includes specialized modules for optimizing computational graphs and managing tensor memory in AI workflows.
- Educational Tool: Ideal for learning compiler construction, with hands-on experience in lexing, parsing, IR generation, and optimization.
- Lightweight Compiler: Once mature, PyC could compile Python-like scripts into efficient binaries for embedded systems or performance-critical applications.
- AI and Scientific Computing: With CUDA integration and graph optimization, PyC targets high-performance AI models and scientific simulations.
- Research Platform: Experimental nature makes it suitable for testing new compiler techniques, optimization strategies, or GPU-based compilation methods.
- Indentation-Based Parsing: Handles Python-like indentation in a C-based compiler, offering a unique challenge.
- GPU-Accelerated Compilation: Uses CUDA for tasks like tokenization, potentially speeding up compilation for large codebases.
- CLI-First Design: Fully operable from the terminal, emphasizing simplicity and transparency.
- Custom Kernel Integration: Allows users to register custom CUDA/OpenCL kernels, enhancing flexibility for specialized computations.
PyC is a platform for learning, innovation, and high-performance computing, bridging compiler design and GPU acceleration for Python-like code.