PyFastUtil 0.1.1

PyFastUtil provides fast, fully Python-compatible data structures along with bindings to low-level interfaces.

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PyFastUtil

Make Python Fast Again

Introduction

PyFastUtil is a high-performance utility library for Python, inspired by Java's FastUtil library. However, PyFastUtil is NOT a Python binding for FastUtil. It is a library built from the ground up, designed to bring C-like efficiency and functionality to CPython.

Features

• Implements all corresponding Python data structure interfaces while significantly improving performance through targeted optimizations. Users can choose the most suitable data structure type based on their needs.
• Fully implemented in C/C++ with hardware-level optimizations such as SIMD, aiming to maximize the performance of data structures.
• Provides efficient Python bindings for some C APIs, allowing advanced users to perform "unsafe" low-level operations.
• Enables inline assembly, allowing advanced users to dynamically generate, invoke, and destroy C functions at runtime.

Benchmark

Note: For extremely fast (O(1)) operations (e.g., pop, extend), PyFastUtil's performance may be slightly inferior to Python's native implementation due to the unavoidable overhead of calling C extensions from CPython. We are actively working to optimize this.

CPU: AMD Ryzen 7 5700G (AVX2)

Windows 11 23H2, Python 3.12, MSVC 19.41.34120

Type-Specialized List (e.g., IntArrayList)

Preparing data...
---Python list & IntArrayList Benchmark---
Batch size: 10000
Repeat: 3

Python list init time: 0.02 ms
PyFastUtil IntArrayList init time: 0.08 ms
PyFastUtil speed of Python list (init): 29.788 %

Python list copy time: 0.02 ms
PyFastUtil IntArrayList copy time: 0.00 ms
PyFastUtil speed of Python list (copy): 766.102 %

Python list to_python time: 0.02 ms
PyFastUtil IntArrayList to_python time: 0.27 ms
PyFastUtil speed of Python list (to_python): 5.821 %

Python list sequential_access time: 0.00 ms
PyFastUtil IntArrayList sequential_access time: 0.00 ms
PyFastUtil speed of Python list (sequential_access): 126.667 %

Python list random_access time: 0.39 ms
PyFastUtil IntArrayList random_access time: 0.62 ms
PyFastUtil speed of Python list (random_access): 62.423 %

Python list sort time: 1.29 ms
PyFastUtil IntArrayList sort time: 0.04 ms
PyFastUtil speed of Python list (sort): 3344.483 %

Python list append time: 0.23 ms
PyFastUtil IntArrayList append time: 0.32 ms
PyFastUtil speed of Python list (append): 72.517 %

Python list insert time: 70.74 ms
PyFastUtil IntArrayList insert time: 10.93 ms
PyFastUtil speed of Python list (insert): 647.349 %

Python list pop time: 0.29 ms
PyFastUtil IntArrayList pop time: 0.35 ms
PyFastUtil speed of Python list (pop): 85.143 %

Python list remove time: 5.50 ms
PyFastUtil IntArrayList remove time: 2.54 ms
PyFastUtil speed of Python list (remove): 216.749 %

Python list contains time: 258.02 ms
PyFastUtil IntArrayList contains time: 2.61 ms
PyFastUtil speed of Python list (contains): 9896.599 %

Python list index time: 438.12 ms
PyFastUtil IntArrayList index time: 3.77 ms
PyFastUtil speed of Python list (index): 11618.038 %

Python list extend time: 0.08 ms
PyFastUtil IntArrayList extend time: 0.14 ms
PyFastUtil speed of Python list (extend): 58.433 %

Python list reverse time: 0.00 ms
PyFastUtil IntArrayList reverse time: 0.00 ms
PyFastUtil speed of Python list (reverse): 378.948 %


Avg speed of PyFastUtil compared to Python list: 1950.647 %

Generic Type List (e.g., ObjectArrayList)

Preparing data...
---Python list & ObjectArrayList Benchmark---
Batch size: 10000
Repeat: 3

Python list init time: 0.06 ms
PyFastUtil ObjectArrayList init time: 0.05 ms
PyFastUtil speed of Python list (init): 129.484 %

Python list copy time: 0.07 ms
PyFastUtil ObjectArrayList copy time: 0.03 ms
PyFastUtil speed of Python list (copy): 268.376 %

Python list to_python time: 0.03 ms
PyFastUtil ObjectArrayList to_python time: 0.03 ms
PyFastUtil speed of Python list (to_python): 99.325 %

Python list sequential_access time: 0.00 ms
PyFastUtil ObjectArrayList sequential_access time: 0.00 ms
PyFastUtil speed of Python list (sequential_access): 139.130 %

Python list random_access time: 0.22 ms
PyFastUtil ObjectArrayList random_access time: 0.31 ms
PyFastUtil speed of Python list (random_access): 71.053 %

Python list sort time: 1039.91 ms
PyFastUtil ObjectArrayList sort time: 1003.55 ms
PyFastUtil speed of Python list (sort): 103.623 %

Python list append time: 0.27 ms
PyFastUtil ObjectArrayList append time: 0.33 ms
PyFastUtil speed of Python list (append): 81.091 %

Python list insert time: 72.43 ms
PyFastUtil ObjectArrayList insert time: 20.82 ms
PyFastUtil speed of Python list (insert): 347.893 %

Python list pop time: 0.36 ms
PyFastUtil ObjectArrayList pop time: 0.27 ms
PyFastUtil speed of Python list (pop): 130.323 %

Python list remove time: 5.17 ms
PyFastUtil ObjectArrayList remove time: 5.17 ms
PyFastUtil speed of Python list (remove): 100.102 %

Python list contains time: 665.64 ms
PyFastUtil ObjectArrayList contains time: 3.77 ms
PyFastUtil speed of Python list (contains): 17634.553 %

Python list index time: 985.46 ms
PyFastUtil ObjectArrayList index time: 6.80 ms
PyFastUtil speed of Python list (index): 14501.919 %

Python list extend time: 0.08 ms
PyFastUtil ObjectArrayList extend time: 0.12 ms
PyFastUtil speed of Python list (extend): 73.264 %

Python list reverse time: 0.00 ms
PyFastUtil ObjectArrayList reverse time: 0.00 ms
PyFastUtil speed of Python list (reverse): 106.000 %


Avg speed of PyFastUtil compared to Python list: 2413.296 %

Installation

Install PyFastUtil from PyPI:

pip install PyFastUtil

Or, build from source

If you'd like to build the project from source, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/PyFastUtil.git
   cd PyFastUtil
   

2. Build the project:

   • On Windows:

     ./build.cmd
     

   • On Linux/macOS:

     ./build.sh
     

License

This project is licensed under the Apache License 2.0. For more details, see the LICENSE file.

Roadmap

• Implement int, float, and double ArrayList and LinkedList.
• Add Numpy support.
• Provide bindings for SIMD utility functions.
• Provide raw AVX512 bindings.
• Perform comprehensive testing and benchmarking.
• Publish to PyPI.

Contribution

Contributions are welcome! Feel free to submit issues or pull requests. Please note that the project is in its early stages, and we greatly appreciate any feedback or suggestions.

Acknowledgements

This project is partially based on the following excellent open-source projects:

• CPython: The official implementation of the Python interpreter, licensed under the Python Software Foundation License.
• C++ Standard Library (STL): The standard library for C++, providing essential data structures, algorithms, and utilities, licensed under the ISO C++ Standard.
• cpp-TimSort: A C++ implementation of the TimSort algorithm, licensed under the MIT License.
• ankerl::unordered_dense: A modern, high-performance, low-memory hash table implementation in C++, licensed under the MIT License.
• qReverse: A high-performance, architecture-optimized array reversal algorithm, released under the MIT License.

Special thanks to the contributors of these open-source projects!

The mascot image of the project was created by the artist kokola and is used with permission. You can view the original artwork on X.