BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230831T095747Z
LOCATION:Sertig
DTSTART;TZID=Europe/Stockholm:20230628T143000
DTEND;TZID=Europe/Stockholm:20230628T150000
UID:submissions.pasc-conference.org_PASC23_sess109_msa259@linklings.com
SUMMARY:Getting the Best of Both Worlds: Bridging Fortran and Pytorch for 
 the CPU and the GPU HPC Simulations
DESCRIPTION:Minisymposium\n\nDmitry Alexeev and Markus Hrywniak (NVIDIA In
 c.)\n\nOver the past years, machine learning (ML) has been attracting rapi
 dly increasing interest in the computational science. Many developers are 
 adding ML models to their traditional simulation pipelines, and yet more a
 re willing to follow. However, integration of such models into an existing
  application is often a technical challenge, which hinders research progre
 ss and demands domain scientists to deal with the programming issues. Thes
 e complications are especially pronounced when the application is written 
 in a language uncommon and not well supported in the ML community, such as
  Fortran. We present a lightweight library that enables seamless invocatio
 n of the Pytorch models from the high-performance Fortran codes. Users can
  run zero-copy inference and training from within their Fortran applicatio
 ns with the model defined via Pytorch Python scripts. The library supports
  CPU and GPU backends and is compatible with OpenACC-accelerated codes. We
  believe that our work could facilitate ML research in the Fortran computa
 tional community.\n\nDomain: Climate, Weather and Earth Sciences\n\nSessio
 n Chair: Tobias Weigel (German Climate Computing Centre, DKRZ)
END:VEVENT
END:VCALENDAR