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...

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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
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Abstract 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.
AbstractList 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.
Author Gligoric, Milos
Erez, Mattan
Zhu, Steven
AlAwar, Nader
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  givenname: Nader
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  surname: Gligoric
  fullname: Gligoric, Milos
  email: gligoric@utexas.edu
  organization: The University of Texas,Austin
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Snippet Traditionally, high performance kernels (HPKs) have been written in statically typed languages, such as C/C++ and Fortran. A recent trend among...
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SubjectTerms bindings
C++ languages
Codes
dynamic program analysis
Generators
Graphics processing units
high performance kernels
Hybrid power systems
Kernel
Python
Title Dynamic Generation of Python Bindings for HPC Kernels
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