1 research outputs found
Toward Efficient Interactions between Python and Native Libraries
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.
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.Comment: In Proceedings of the 29th ACM Joint European Software Engineering
Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
2021), August 23-27, 2021, Athens, Greece. ACM, New York,NY, USA, 12 page