Toward Efficient Interactions between Python and Native Libraries [article]

Jialiang Tan, Yu Chen, Zhenming Liu, Bin Ren, Shuaiwen Leon Song, Xipeng Shen, Xu Liu
2021 pre-print
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
more » ... from its execution behavior. On the other side, native libraries do not include program semantics to understand algorithm defects. To understand the interaction inefficiencies, we extensively study a large collection of Python software packages and categorize them according to the root causes of inefficiencies. We extract two inefficiency patterns that are common in interaction inefficiencies. Based on these patterns, we develop PieProf, a lightweight profiler, to pinpoint interaction inefficiencies in Python applications. The principle of PieProf is to measure the inefficiencies in the native execution and associate inefficiencies with high-level Python code to provide a holistic view. Guided by PieProf, we optimize 17 real-world applications, yielding speedups up to 6.3× on application level.
doi:10.1145/3468264.3468541 arXiv:2107.00064v1 fatcat:wasn3qtbrvdtnmf77dng3utfgm