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DTSTAMP:20230831T095745Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T112000
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UID:submissions.pasc-conference.org_PASC23_sess104_pos140@linklings.com
SUMMARY:P29 - GT4Py: A Python Framework for the Development of High-Perfor
 mance Weather and Climate Applications
DESCRIPTION:Poster\n\nMauro Bianco and Till Ehrengruber (ETH Zurich / CSCS
 ); Nicoletta Farabullini and Abishek Gopal (ETH Zurich); Linus Groner and 
 Rico Häuselmann (ETH Zurich / CSCS); Peter Kardos, Samuel Kellerhals, and 
 Magdalena Luz (ETH Zurich); Christoph Müller (MeteoSwiss); Enrique G. Pare
 des (ETH Zurich / CSCS); Matthias Roethlin (MeteoSwiss); Felix Thaler and 
 Hannes Vogt (ETH Zurich / CSCS); Benjamin Weber (MeteoSwiss); and Thomas C
 . Schulthess (ETH Zurich / CSCS)\n\nGT4Py is a Python framework for weathe
 r and climate applications simplifying the development and maintenance of 
 high-performance codes in prototyping and production environments. GT4Py s
 eparates model development from hardware architecture dependent optimizati
 ons, instead of intermixing both together in source code, as regularly don
 e in lower-level languages like Fortran, C, or C+. Domain scientists focus
  solely on numerical modeling using a declarative embedded domain specific
  language supporting common computational patterns of dynamical cores and 
 physical parametrizations. An optimizing toolchain then transforms this hi
 gh-level representation into a finely-tuned implementation for the target 
 hardware architecture. This separation of concerns allows performance engi
 neers to implement new optimizations or support new hardware architectures
  without requiring changes to the application, increasing productivity for
  domain scientists and performance engineers alike. We will present recent
  developments in the project: support for non-cartesian meshes, a new prog
 ramming interface enabling operator composition, and a redesigned intermed
 iate representation that separates stateful from stateless computations, s
 implifying the creation of optimization passes. We further showcase a perf
 ormance comparison of the ICON model between the original Fortran implemen
 tation using openACC and a GT4Py-enabled version developed as part of the 
 EXCLAIM project.\n\nSession Chair: Elaine M. Raybourn (Sandia National Lab
 oratories)
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