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Combined Python/CUDA JIT for Flexible Acceleration in RAPIDS

Jiqun Tu, Nvidia Corporation

GTC 2020

We'll introduce our design and implementation of a framework within RAPIDS/cuDF that enables compiling Python user-defined functions and inlining them into native CUDA kernels. Our framework uses the Numba Python compiler and Jitify CUDA just-in-time (JIT) compilation library to provide cuDF users the flexibility of Python with the performance of CUDA as a compiled language. An essential part of the framework is a parser that parses the CUDA PTX function, which is compiled from the Python UDF, into an equivalent CUDA device function that can be inlined into native CUDA C++ kernels. Learn how our approach makes it possible for non-expert Python users to extend optimized dataframe operations with their own Python UDFs, and enables more flexibility and generality for high-performance computations on dataframes in RAPIDS.

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