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Publications of Torsten Hoefler
Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler:

 Productivity, Portability, Performance: Data-Centric Python

(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21), Nov. 2021)

Publisher Reference

Abstract

Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes.

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BibTeX

@inproceedings{,
  author={ Alexandros Nikolaos Ziogas and Timo Schneider and Tal Ben-Nun and Alexandru Calotoiu and Tiziano De Matteis and Johannes de Fine Licht and Luca Lavarini and Torsten Hoefler},
  title={{Productivity, Portability, Performance: Data-Centric Python}},
  year={2021},
  month={Nov.},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)},
  source={http://www.unixer.de/~htor/publications/},
}


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