Productivity, Portability, Performance: Data-Centric Python

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. Howe...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-08
Main Authors: Ziogas, Alexandros Nikolaos, Schneider, Timo, Ben-Nun, Tal, Calotoiu, Alexandru, De Matteis, Tiziano, de Fine Licht, Johannes, Lavarini, Luca, Hoefler, Torsten
Format: Article
Language:English
Subjects:
Computation
Field programmable gate arrays
Productivity
Programming languages
Workflow
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2107.00555