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DTSTAMP:20211207T054814Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211118T163000
DTEND;TZID=America/Chicago:20211118T170000
UID:submissions.supercomputing.org_SC21_sess173_pap301@linklings.com
SUMMARY:Productivity, Portability, Performance: Data-Centric Python
DESCRIPTION:Paper\n\nProductivity, Portability, Performance: Data-Centric 
 Python\n\nZiogas, Schneider, Ben-Nun, Calotoiu, De Matteis...\n\nPython ha
 s become the de facto language for scientific computing. Programming in Py
 thon is highly productive, mainly due to its rich science-oriented softwar
 e ecosystem built around the NumPy module. As a result, the demand for Pyt
 hon support in high-performance computing (HPC) has skyrocketed. The Pytho
 n language itself, however, does not necessarily offer high performance. I
 n 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 representat
 ion. 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 speedu
 ps over previous-best solutions; first-ever Xilinx and Intel FPGA results 
 of annotated Python; and up to 93.16% scaling efficiency on 512 nodes.\n\n
 Tag: Reproducibility Badge, Algorithms, Cloud and Distributed Computing, D
 ata Management, Parallel Programming Languages and Models\n\nRegistration 
 Category: Tech Program Reg Pass\n\nReproducibility Badges: Artifact Availa
 ble, Artifact Functional, Results Reproduced
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