An Empirical Study on the Performance and Energy Usage of Compiled Python Code

TL;DR

This study empirically compares the performance and energy efficiency of various Python compilers across multiple benchmarks, revealing significant improvements and variability depending on the compiler and code characteristics.

Contribution

It provides a comprehensive analysis of how different Python compilers affect performance and energy consumption, considering factors like code characteristics and hardware settings.

Findings

Codon, PyPy, and Numba achieve over 90% improvements in speed and energy efficiency.

Nuitka consistently optimizes memory usage across testbeds.

Compilation effects on LLC miss rate vary significantly across benchmarks.

Abstract

Python is a popular programming language known for its ease of learning and extensive libraries. However, concerns about performance and energy consumption have led to the development of compilers to enhance Python code efficiency. Despite the proven benefits of existing compilers on the efficiency of Python code, there is limited analysis comparing their performance and energy efficiency, particularly considering code characteristics and factors like CPU frequency and core count. Our study investigates how compilation impacts the performance and energy consumption of Python code, using seven benchmarks compiled with eight different tools: PyPy, Numba, Nuitka, Mypyc, Codon, Cython, Pyston-lite, and the experimental Python 3.13 version, compared to CPython. The benchmarks are single-threaded and executed on an NUC and a server, measuring energy usage, execution time, memory usage, and Last-Level Cache (LLC) miss rates at a fixed frequency and on a single core. The results show that compilation can significantly enhance execution time, energy and memory usage, with Codon, PyPy, and Numba achieving over 90\% speed and energy improvements. Nuitka optimizes memory usage consistently on both testbeds. The impact of compilation on LLC miss rate is not clear since it varies considerably across benchmarks for each compiler. Our study is important for researchers and practitioners focused on improving Python code performance and energy efficiency. We outline future research directions, such as exploring caching effects on energy usage. Our findings help practitioners choose the best compiler based on their efficiency benefits and accessibility.