一句话：指出 Python 与 C/C++/Fortran 原生库交互因边界抽象而性能受损，并探索跨语言语义与执行行为协调。
Python 因可编程性流行，许多软件用 Python 做高层算法设计，并依赖 C/C++/Fortran 原生库执行高效计算内核。然而，Python 与原生库边界上的抽象会引入性能损失：一方面，通常以解释方式运行的 Python 代码与其真实执行行为脱节；另一方面，原生库缺少程序语义，难以理解算法缺陷。论文面向这一交互效率问题，旨在揭示并缓解跨语言边界的性能与语义割裂。摘要仅给出问题动机，未说明具体方法、实验设计与结果；因此无法确认其技术路线与量化收益。其价值在于将 Python 解释执行与原生库语义理解联系起来，为后续跨语言运行时、分析与优化工作提出方向。
Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend on native libraries written in C/C++/Fortran for efficient computation kernels. Interaction between Python code and native libraries introduces performance losses because of the abstraction lying on the boundary of Python and native libraries. On the one side, Python code, typically run with interpretation, is disjoint from its execution behavior. On the other side, native libraries do not include program semantics to understand algorithm defects.
@inproceedings{paperbot6348,
title = {Toward efficient interactions between Python and native libraries},
author = {Jialiang Tan and Yu Chen and Zhenming Liu and Bin Ren and Shuaiwen Leon Song and Xipeng Shen and Xu Liu},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468541}
}