11-16, 14:00–14:30 (Asia/Hong_Kong), LT8
Language: English
- Numba is a popular JIT compiler that translates Python code into optimized machine code for various hardware targets, and Numba-CUDA supports compilation of Python code for execution on NVIDIA devices. Whilst Numba-CUDA provides many basic accelerated programming function blocks out of the box, manually creating bindings for a CUDA device library is still laborious.
- Numbast is an auto device binding generation tool created by NVIDIA. Numbast provides an end-to-end binding generation mechanism that quickly bridges the gap between the CUDA ecosystem and Python CUDA.
- In this talk, attendees will learn about recent progress of accelerated computing in Python with Numba-CUDA, the internal mechanisms of Numbast, and get hands-on experience of crafting CUDA kernels in Numba-CUDA, as well as creating bindings with Numbast.
- Additionally, we will provide an insight into how Numba is used across RAPIDS, Nvidia’s accelerated computing solution that focuses on making accelerated computing more accessible to the general python community. Time permitting, we will also introduce how user-defined functions (UDF) are used in
cudf.pandas.
See Abstract.
Michael Yh Wang is a software engineer in Nvidia Rapids. He currently contributes his engineering skills towards cuDF, cuSpatial and Numba. Prior to Nvidia he acquired a master of science degree from Yale University. His earlier experience includes working as a visual effect supervisor at an independent film project, and achieving first place at the WAIC 2020 hackathon competition. Michael has strong interests in software engineering, computer graphics algorithms and compiler technology. He believes in a future where accelerated computing can be brought more accessible to the public via compiler and language innovations.