Welcome to the Graph Theory Benchmark repository! This project aims to provide a comprehensive comparison of graph theory implementations in Python and Rust. Graph theory is a fundamental field in computer science with various applications, from network analysis to social network algorithms. Understanding the performance characteristics of different programming languages is crucial for optimizing graph algorithms for real-world applications.
Python Implementation: We have implemented a variety of graph algorithms in Python, including breadth-first search, depth-first search, Dijkstra's algorithm, and more. These implementations serve as our baseline for comparison.
Rust Implementation: In Rust, we have developed equivalent graph algorithms, taking advantage of Rust's low-level capabilities and performance optimizations.
Benchmarking: The heart of this repository is the benchmarking suite that rigorously tests both Python and Rust implementations across various graph sizes, structures, and use cases. We measure execution times, memory usage, and other relevant metrics.
Detailed Comparisons: We provide detailed performance comparisons and analysis, helping you understand the trade-offs between Python and Rust for different graph-related tasks.
Documentation: Our documentation explains how to use the benchmarking suite, interpret the results, and optimize your code based on the findings.
Why Rust and Python? Python is a popular language for rapid development and prototyping, but it may not always provide the best performance for compute-intensive tasks. Rust, on the other hand, is known for its speed, safety, and low-level control. By comparing these two languages, we aim to help you make informed decisions when selecting the right tool for your graph-related projects.