mainpage.dox

/**
 * @mainpage Zip and Skip Tries
 * 
 * @section intro_sec Introduction
 * 
 * This project implements and evaluates several trie data structures with a focus on performance and memory efficiency:
 * 
 * - **SkipTrie**: A sequential trie implementation that uses skipping techniques to improve traversal speed
 * - **ZipTrie**: A memory-efficient trie that compresses paths with single children
 * - **ParallelSkipTrie**: A GPU-accelerated version of SkipTrie using CUDA
 * - **ParallelZipTrie**: A GPU-accelerated version of ZipTrie using CUDA
 * 
 * Each implementation is designed to efficiently store and search for strings, with particular optimizations for genetic sequence data and other applications with long common prefixes.
 * 
 * @section features_sec Key Features
 * 
 * - **Memory Efficiency**: ZipTrie variants compress paths to reduce memory usage
 * - **GPU Acceleration**: Parallel implementations leverage CUDA for high-performance operations
 * - **Genetic Data Support**: Specialized support for nucleotide sequences from the ABC-HuMi dataset
 * - **Synthetic Data Generation**: Tools for creating controlled test data with specific properties
 * - **Comprehensive Benchmarking**: Detailed performance analysis across different parameters
 * 
 * @section architecture_sec Architecture
 * 
 * The codebase is organized into several key components:
 * 
 * - **Core Data Structures**: Implementation of the trie variants (SkipTrie, ZipTrie, ParallelSkipTrie, ParallelZipTrie)
 * - **BitString**: Fundamental representation of strings optimized for trie operations
 * - **CUDA Utilities**: Helper functions for GPU memory management and kernel execution
 * - **Genetic Data Processing**: Tools for loading and processing genetic sequences
 * - **Synthetic Data Generation**: Utilities for creating test data with controlled properties
 * - **Benchmarking**: Comprehensive performance measurement and analysis tools
 * 
 * @section usage_sec Usage
 * 
 * The project includes several executable components:
 * 
 * - **verify**: Validates the correctness of all trie implementations
 * - **genetic_benchmark**: Benchmarks performance on genetic data from the ABC-HuMi dataset
 * - **synthetic_benchmark**: Benchmarks performance on synthetic data with controlled properties
 * - **plot_genetic.py** and **plot_synthetic.py**: Generate visualizations of benchmark results
 * 
 * @section build_sec Building
 * 
 * The project uses a Makefile build system and requires:
 * 
 * - CUDA Toolkit (for parallel implementations)
 * - C++17 compatible compiler
 * - Python with matplotlib (for plotting)
 * 
 * Basic build commands:
 * ```bash
 * make verify            # Build the verification program
 * make genetic_benchmark # Build the genetic data benchmark
 * make synthetic_benchmark # Build the synthetic data benchmark
 * ```
 * 
 * @section benchmarking_sec Benchmarking
 * 
 * The project includes comprehensive benchmarking capabilities:
 * 
 * 1. Run the benchmark executables to collect performance data:
 *    ```bash
 *    ./genetic_benchmark <num_trials> <num_simulations>
 *    ./synthetic_benchmark <num_words> <word_length> <mean_lcp> <num_repetitions>
 *    ```
 * 
 * 2. Generate visualization plots:
 *    ```bash
 *    python plot_genetic.py
 *    python plot_synthetic.py
 *    ```
 * 
 * @section performance_sec Performance
 * 
 * The parallel implementations show significant speedups over sequential versions, particularly for:
 * 
 * - Large datasets (millions of strings)
 * - Long strings (thousands of characters)
 * - Strings with long common prefixes
 * 
 * The memory-efficient variants reduce memory usage at a small cost to performance, making them suitable for memory-constrained environments.
 */