BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Denver
X-LIC-LOCATION:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20200129T163559Z
LOCATION:502-503-504
DTSTART;TZID=America/Denver:20191118T153000
DTEND;TZID=America/Denver:20191118T162000
UID:submissions.supercomputing.org_SC19_sess115_pec228@linklings.com
SUMMARY:Afternoon Keynote - Running large models in minutes: an engineerin
 g journey through high performance for AI
DESCRIPTION:Workshop\n\nAfternoon Keynote - Running large models in minute
 s: an engineering journey through high performance for AI\n\nBernauer\n\nF
 rom climate modelling to drug design, AI models are not fully part of scie
 ntific modelling and AI models are getting more complex and larger every y
 ear. The adoption of challenging workloads like the BERT language model an
 d the popularity of Deep Learning performance blogs or benchmarks such as 
 MLPerf highlight the importance of being able to quickly train and tune su
 ch models. Until recently, system design for HPC and AI were often done in
  isolation as the requirements for the platforms where different, making l
 arge scientific experimentations difficult. To overcome these gaps, system
 s are now designed with AI software in mind and scale is introduced in the
  software design from ground up so that each model running at the edge can
  be trained in minutes at scale. In this talk we will cover how software l
 everages the inherent scaling nature of large models and how HPC infrastru
 ctures can be built and leveraged as the ideal platforms for fast experime
 ntation and large problems.\n\nTag: Workshop Reg Pass, Machine Learning\n\
 nRegistration Category: Workshop Reg Pass, Machine Learning
URL:https://sc19.supercomputing.org/presentation/?id=pec228&sess=sess115
END:VEVENT
END:VCALENDAR

