Welcome to Python-Numba-vs-Other-Languages GitHub repository! This repository contains implementations of various algorithms and tasks comparing the performance of Python with Numba against other popular programming languages such as C++, C#, JavaScript, and Rust. The primary goal is to showcase the speed and efficiency of Python when accelerated with Numba, especially when handling computationally intensive tasks.
Python is a versatile and widely-used language known for its ease of use and readability. However, its interpreted nature can sometimes lead to performance bottlenecks when dealing with complex computations or large datasets. Numba is a just-in-time (JIT) compiler that can significantly boost Python's execution speed by translating Python code to optimized machine code, making it comparable in performance to other compiled languages.
The repository contains multiple directories, each representing a tasks or algorithms implemented with some programming language including Python with Numba, C++, C#, JavaScript, and Rust. The implementations are designed to perform the same task or solve the same problem, allowing you to directly compare their execution times.
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Clone the repository to your local machine.
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Navigate to the specific task or algorithm implemented in programming language you want to explore.
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Review the code implementations in different languages, including Python with Numba.
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Run the code and measure the execution times to observe the performance differences.
Contributions to this repository are welcome! If you have additional algorithms, tasks or programming languages to compare, or if you have code optimizations or improvements, please feel free to create a pull request. Let's collaborate to expand the collection and share insights into Python with Numba's capabilities.
The goal of this repository is to demonstrate the relative performance between Python with Numba and other languages for specific tasks. The results may vary based on the complexity of the code, hardware, and compiler optimizations. Always consider the specific requirements and characteristics of your projects when choosing the best programming language for a given task.
For a more details, you can read the full article:
On Medium: "Numba: Unleashing the Power of Python for High-Performance Computing"
On My Blog: "Numba: Unleashing the Power of Python for High-Performance Computing"
This repository is licensed under the MIT License. See the LICENSE file for more details.
Thank you for visiting Python-Numba-vs-Other-Languages repository! We hope this collection of code implementations and performance comparisons will be useful in understanding the benefits of leveraging Numba in Python for faster and more efficient computations. Happy coding and optimizing!