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DTSTAMP:20230831T095746Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230627T102100
DTEND;TZID=Europe/Stockholm:20230627T102200
UID:submissions.pasc-conference.org_PASC23_sess110_pos132@linklings.com
SUMMARY:P49 - Simulating Aquaplanet Using ICON with a GT4Py DSL Dynamical 
 Core
DESCRIPTION:Poster\n\nChristoph Müller (MeteoSwiss); Abishek Gopal (ETH Zu
 rich / CSCS); Nicoletta Farabullini (ETH Zurich); Till Ehrengruber (ETH Zu
 rich / CSCS); Samuel Kellerhals, Peter Kardos, and Magdalena Luz (ETH Zuri
 ch); Matthias Röthlin (MeteoSwiss); Enrique G. Paredes (ETH Zurich / CSCS)
 ; David Leutwyler and Benjamin Weber (MeteoSwiss); Rico Häuselmann and Fel
 ix Thaler (ETH Zurich / CSCS); Jonas Jucker (ETH Zurich); Linus Groner, Ha
 nnes Vogt, and Mauro Bianco (ETH Zurich / CSCS); Anurag Dipankar (ETH Zuri
 ch); and Carlos Osuna and Xavier Lapillonne (MeteoSwiss)\n\nWe present the
  results of our efforts porting the dynamical core of the ICON climate and
  numerical weather prediction (NWP) model to GT4Py. GT4Py is a Domain Spec
 ific Language (DSL) designed for weather and climate applications, which a
 llows domain scientists to write performance portable climate and weather 
 code within a high level Python-based frontend. Porting code to GT4Py grea
 tly improves readability as compared to equivalent GPU-accelerated codes w
 ritten in Fortran + OpenACC. Additionally the DSL allows for a separation 
 of concerns between the domain scientists, software engineers and optimiza
 tion experts. The fine grained and automatic integration of the DSL genera
 ted code back into the Fortran ICON code enables us to compare the origina
 l Fortran code to the DSL generated code by running both versions. We call
  this the verification mode of the integration. On the other hand, in the 
 substitution mode, only the DSL generated version of the code is executed.
  After a thorough verification process of the model porting, we present re
 sults from the Aquaplanet idealized experiment on a global icosahedral gri
 d of resolution ~80km. We also compare the performance of ICON with GT4Py 
 dycore with the standard ICON-NWP model running on GPUs.\n\nSession Chair:
  Jibonananda Sanyal (National Renewable Energy Laboratory)
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