Dynamic generation of Python bindings for HPC kernels

Zhu, Junlin Steven
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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, WayOut 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.