Dynamic Generation of Python Bindings for HPC Kernels

Traditionally, high performance kernels (HPKs) have been written in statically typed languages, such as C/C++ and Fortran. A recent trend among scientists-prototyping applications in dynamic languages such as Python-created a gap between the applications and existing HPKs. Thus, scientists have to e...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 92 - 103
Main Authors: Zhu, Steven, AlAwar, Nader, Erez, Mattan, Gligoric, Milos
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2021
Subjects:
bindings
C++ languages
Codes
dynamic program analysis
Generators
Graphics processing units
high performance kernels
Hybrid power systems
Kernel
Python
ISSN:2643-1572
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditionally, high performance kernels (HPKs) have been written in statically typed languages, such as C/C++ and Fortran. A recent trend among scientists-prototyping applications in dynamic languages such as Python-created a gap between the applications and existing HPKs. Thus, scientists have to either reimplement necessary kernels or manually create a connection layer to leverage existing kernels. Either option requires substantial development effort and slows down progress in science. We present a technique, dubbed WayOut, which automatically generates the entire connection layer for HPKs invoked from Python and written in C/C++. WayOut performs a hybrid analysis: it statically analyzes header files to generate Python wrapper classes and functions, and dynamically generates bindings for those kernels. By leveraging the type information available at run-time, it generates only the necessary bindings. We evaluate WayOut by rewriting dozens of existing examples from C/C++ to Python and leveraging HPKs enabled by WayOut. Our experiments show the feasibility of our technique, as well as negligible performance overhead on HPKs performance.
ISSN:2643-1572
DOI:10.1109/ASE51524.2021.9678726