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Productivity, Portability, Performance: Data-Centric Python

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arxiv 2107.00555 v2 pith:MGV6CB5P submitted 2021-07-01 cs.PL cs.DCcs.PF

Alexandros Nikolaos Ziogas , Timo Schneider , Tal Ben-Nun , Alexandru Calotoiu , Tiziano De Matteis , Johannes de Fine Licht , Luca Lavarini , Torsten Hoefler This is my paper · ORCID
classification cs.PLcs.DCcs.PF
keywords pythonperformancehighlanguageacrosscomputingdata-centricfpga
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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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