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:20190719T085744Z
LOCATION:HG D 1.1
DTSTART;TZID=Europe/Stockholm:20190613T141500
DTEND;TZID=Europe/Stockholm:20190613T144500
UID:submissions.pasc-conference.org_PASC19_sess115_msa293@linklings.com
SUMMARY:Performance Modeling and Scalability for the ICON Model
DESCRIPTION:Minisymposium\nClimate and Weather\n\nPerformance Modeling and
  Scalability for the ICON Model\n\nNeumann, Adamidis, Biercamp\n\nGlobal k
 ilometre-scale resolving weather and climate models come with extreme comp
 ute requirements. Profiling, understanding and finally predicting the perf
 ormance and scalability of these models is hence utterly important to effi
 ciently exploit today’s supercomputers on the one hand and to achiev
 e optimal time-to-solution in weather and climate predictions on the other
  hand. The Centre of Excellence in Simulation of Weather and Climate in Eu
 rope (ESiWACE) focuses on the development of global high-resolution models
 , which have fed into the international intercomparison project DYAMOND. A
  key challenge in these developments lies - besides the scientific case of
  DYAMOND - in the prediction of the performance of models used in the comm
 unities on upcoming exascale systems. I will present recent considerations
  on measuring and predicting performance of weather and climate models at 
 the example of the ICOsahedral Non-hydrostatic (ICON) model. After introdu
 cing ESiWACE and DYAMOND, I will discuss scalability of high-resolution IC
 ON runs and present a semi-analytical performance modeling approach to pre
 dict ICON scalability, given hardware properties and some additional knowl
 edge on the model run configuration. I will close with the presentation of
  the sparse grid regression technique to predict performance from measured
  scalability data.
END:VEVENT
END:VCALENDAR