2025/06/30 by Isabella Basso Amaral, Amaral, Isabella Basso do, Renato Cordeiro Ferreira +3
Computer Science · Decision Sciences · #B.8 #Computational Physics and Python Applications #D.2.13 #D.2.8 #D.3.3 #Distributed #FOS: Computer and information sciences #Parallel #Performance (cs.PF) #Programming Languages (cs.PL) #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Research #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2507.00264
openalex publication_date 2025/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Python programming language is best known for its syntax and scientific libraries, but it is also notorious for its slow interpreter. Optimizing critical sections in Python entails special knowledge of the binary interactions between programming languages, and can be cumbersome to interface manually, with implementers often resorting to convoluted third-party libraries. This comparative study evaluates the performance and ease of use of the PyO3 Python bindings toolchain for Rust against ctypes and cffi. By using Rust tooling developed for Python, we can achieve state-of-the-art performance with no concern for API compatibility.