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‎README.md‎

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# ♟️ Chessboard Engine
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# Chess Engine
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A chess engine written in **C**, using **bitboards** and tested with **Criterion**.
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It can load FEN positions, generate all legal moves, and run **perft tests** to validate move generation.
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---
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## 📂 Project Structure
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├── assets/ # Resources (images, ...)
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├── lib/
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│ └── chessboard/ # Engine headers
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├── src/
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│ └── chessboard/ # Engine source code
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├── tests/ # Unit tests (Criterion)
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├── Makefile # Build system
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└── README.md # This file
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---
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## ⚡ Installation & Build
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### Requirements
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- **gcc** or **clang**
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- **make**
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- **Criterion** for tests
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```sh
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sudo apt-get install libcriterion-dev
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```
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### Build with GUI
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```sh
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make ui
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```
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### Run the engine
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```sh
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make run
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```
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### Run tests
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```sh
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make test
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```
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A high-performance chess engine written in C, featuring an advanced move generation system, graphical interface, and bot API for automated gameplay.
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## Features
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- Load positions via FEN ✅
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- Generate all legal moves ✅
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- Play a game through a GUI ✅
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- Validate move generation using perft tests ✅
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- Position evaluation (WIP)
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- Search engine (minimax / alpha-beta, WIP)
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### 🧪 CI/CD
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This project is automatically tested with GitHub Actions.
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Unit tests are executed on every push and pull request.
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Move generation is validated using perft tests.
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👉 [Perft results reference](https://www.chessprogramming.org/Perft_Results)
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## 🚀 Optimizations
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To improve performance and speed up perft tests, several optimizations have been implemented:
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- Bitboards: 64-bit masks to represent the board, enabling fast operations with bitwise logic.
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### 🚀 High-Performance Move Generation
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- **Bitboard representation** for efficient board state management
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- **Magic bitboards** for O(1) sliding piece move generation
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- **Multi-threaded perft testing** achieving up to 80 million moves per second
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- Compact move structures optimized for cache efficiency
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### 🎮 Graphical Interface
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- Interactive chess board with visual piece movement
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- Human vs Bot gameplay
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- Bot vs Bot gameplay
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### 🤖 Bot API
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- bot communication
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- Support for bot vs bot matches
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- Standardized move format and game state representation
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- Easy integration for custom chess engines
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### 🧠 Chess Engine
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- Legal move generation with full rule support
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- Evaluation function for position assessment
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- Search algorithms for move selection
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- Support for standard chess notation
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## Getting Started
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### Prerequisites
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- GCC or compatible C compiler
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- Make build system
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- POSIX threads support (for multi-threading)
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- SDL2 for ui
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### Building the Project
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```bash
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# Clone the repository
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git clone [https://github.com/TomBaran501/chessboardEngine.git]
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cd [chessboardEngine]
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# Build and run the project
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make run
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```
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- Precomputed tables: cached attacks for "normal" pieces (knight, pawn, king) and magic bitboards for sliding pieces (rook, bishop, queen).
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## Documentation
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- Multi-threaded perft: parallelized tree exploration to take advantage of multi-core CPUs.
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Comprehensive documentation is available in the `documentation/` folder:
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- Reduced dynamic allocations: use of preallocated arrays (Move move_list[MAX_MOVES]) to avoid unnecessary memory overhead.
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- **[Move Generation](documentations/legal_move_generation.md)** - Technical details on the legal move generation system, optimization techniques, and performance benchmarks
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- **[Bot API](documentations/bot_api.md)** - API specification for developing and connecting chess bots
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- **[Main Commands](documentations/commands_main.md)** - Command-line interface reference and usage guide
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### ⚡ Performance:
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## Usage
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Single-thread speed: 8–10 million moves/s
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### Playing Against the Bot
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Multi-thread speed: 40–90 million moves/s
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(⚠️ Multi-threading is less efficient when the root position has fewer legal moves)
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Launch the graphical interface and play against the built-in chess engine. Use the documented commands to control the game flow.
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## Future Work
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### Running Bot vs Bot Matches
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Planned improvements and features to make the engine stronger and more complete:
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Start the API server and connect your bots to compete against each other. See the [Bot API documentation](documentation/bot_api.md) for integration details.
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#### Search improvements
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### Performance Testing
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- Implement iterative deepening
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Run perft tests to verify move generation correctness and measure performance:
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- Add alpha-beta pruning
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```bash
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./chess
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go perft [depth]
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```
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- Introduce move ordering (killer moves, history heuristic)
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## Project Structure
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#### Position evaluation
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```
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.
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├── src/ # Source code files
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├── lib/ # Header files
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├── documentation/ # Project documentation
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├── tests/ # Test suites
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├── bots/ # Test suites
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├── Makefile # Build configuration
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└── README.md # This file
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- Material and piece-square tables
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- King safety and pawn structure evaluation
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## Performance Benchmarks
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- Mobility and control of the center
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- **Move generation**: Up to 80 million moves/second (perft)
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- **Evaluation speed**: Between 5 millions and 40 millions positions evaluated per second
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#### Transposition tables
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## Technical Highlights
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- Store already-evaluated positions using Zobrist hashing
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- Efficient bitboard-based board representation
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- Magic bitboard move generation for sliding pieces
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- Multi-threaded perft calculations
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- Cache-optimized data structures
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- Minimal memory footprint per move
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- Reduce redundant calculations in search
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#### UCI Protocol support
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## License
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- Allow communication with GUIs such as Arena, Cute Chess, or lichess-bot
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MIT License
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Copyright (c) 2025 Baran Tom
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---
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## Acknowledgments
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## 📸 Screenshots
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- Magic bitboard implementation inspired by [Chess Programming Wiki](https://www.chessprogramming.org/)
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- Perft positions from standard test suites
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### 🧵 Multi-threaded Perft Test
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Exemple d’un perft test lancé en multi-threading :
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![Perft Test](assets/screenshots/result_perft_test_multi_threading.png)
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### 🎨 Graphical User Interface
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Interface graphique permettant de jouer une partie :
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![Chessboard UI](assets/screenshots/ihm_chess_engine.png)
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**Note**: This is an educational/hobby project demonstrating advanced techniques in chess programming and game engine development.

‎documentations/legal_moves_generation.md‎

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# Legal Move Generation
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## Overview
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This chess engine implements a highly optimized legal move generation system, achieving performance of up to **80 million moves per second** during perft testing. The implementation leverages modern optimization techniques including bitboards, magic bitboards, efficient data structures, and multi-threading.
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## Core Techniques
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### Bitboards
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The engine uses a bitboard representation where each piece type and color is represented as a 64-bit integer, with each bit corresponding to a square on the board. This approach offers several advantages:
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- **Parallel operations**: Multiple squares can be evaluated simultaneously using bitwise operations
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- **Memory efficiency**: The entire board state fits in CPU cache
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- **Fast move generation**: Bitwise operations (AND, OR, XOR, shifts) are extremely fast at the hardware level
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### Magic Bitboards
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Magic bitboards are used for sliding piece move generation (bishops, rooks, queens). This technique uses pre-computed lookup tables with magic numbers to instantly retrieve possible moves:
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1. **Occupancy masking**: Relevant occupied squares are identified
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2. **Magic multiplication**: A magic number transforms the occupancy into a unique hash
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3. **Table lookup**: The hash indexes into a pre-computed move table
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This eliminates the need for iterative ray-casting and provides O(1) move generation for sliding pieces.
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### Compact Move Structures
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Moves are represented using minimal memory to maximize cache efficiency:
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- Compact bit-packed structures store source square, destination square, move type, and flags
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- Smaller move structures mean more moves fit in cache, reducing memory latency
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- Move lists can be processed more efficiently due to better spatial locality
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### Multi-threading
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The move generation system leverages parallel processing for performance-critical operations:
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- Perft calculations are parallelized across multiple threads
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- Work is distributed efficiently to maximize CPU utilization
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- Thread-safe data structures ensure correctness in concurrent scenarios
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## Performance Testing with Perft
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### What is Perft?
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Perft (performance test) is a standard debugging and benchmarking function in chess programming. It counts all leaf nodes at a given depth in the game tree, serving as both a correctness test and a performance benchmark.
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### Testing Methodology
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The engine's move generation was validated and benchmarked using perft tests:
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1. **Correctness verification**: Results were compared against known perft values from standard positions
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2. **Performance measurement**: Nodes per second (NPS) was measured across various depths
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3. **Regression testing**: Perft suites ensure modifications don't introduce bugs
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### Performance Results
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The implementation achieves **80 million moves per second** during perft testing, demonstrating:
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- Efficient move generation algorithms
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- Effective use of CPU cache through compact data structures
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- Successful parallelization across multiple cores
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- Minimal branching and optimal instruction pipelining
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### Standard Test Positions
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Common perft positions used for validation include:
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- **Starting position**: `rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1`
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- **Kiwipete**: A complex middlegame position testing en passant, castling, and promotions
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- **Position 3-6**: Additional positions from the perft suite covering edge cases
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👉 [Perft results reference](https://www.chessprogramming.org/Perft_Results)
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## Implementation Highlights
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### Move Generation Pipeline
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1. **Bitboard initialization**: Board state is maintained as bitboards
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2. **Attack generation**: Magic bitboards generate sliding piece attacks
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3. **Legal move filtering**: Moves are validated against check and pin constraints
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4. **Move encoding**: Legal moves are packed into compact structures
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5. **Move ordering**: Moves are sorted for optimal alpha-beta pruning (when used in search)
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### Optimization Techniques Applied
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- **Pre-computed lookup tables**: Attack patterns, magic numbers, and ray tables
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- **Bit manipulation intrinsics**: Use of hardware instructions (POPCNT, TZCNT, etc.)
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- **Branch prediction optimization**: Code structured to minimize mispredictions
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- **Memory alignment**: Data structures aligned for optimal CPU access
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- **Copy-make approach**: Efficient board state copying for recursive search
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## Future Improvements
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Potential areas for further optimization:
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- SIMD instructions for parallel bitboard operations
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- GPU acceleration for bulk perft calculations
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- Advanced prefetching strategies
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- Further tuning of magic bitboard tables
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## Conclusion
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The combination of bitboards, magic bitboards, compact data structures, and multi-threading results in a move generation system that is both fast and correct. The 80 million NPS achieved during perft testing demonstrates that the implementation is competitive with modern chess engines and provides a solid foundation for the complete chess engine.

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