Exploring High-Performance Python Using Concurrency and Different Interpreters

Abstract

vii, 44 p.Python has established itself as one of the top programming languages in the last decade, thanks to its ease of learning and versatility across various fields. Its community spans both academia and industry, including areas such as data science, machine learning, web development, and scripting. As the demand for Python continues to grow, there is a strong push to discover methods for enhancing its performance in safe and effective ways. However, this convenience comes with a limitation, the Global Interpreter Lock (GIL), which is a builtin mechanism to synchronize the execution of threads. This feature makes code writing in Python much more user-friendly but restricts Python’s capabilities in distributed and parallel computing. In this SIP, I will study the ways to achieve high-performance Python from coding style to libraries and interpreters. All the code used in this paper can be found at https:// github.com/chauta03/python-parallel.git