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:20210916T132446Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210705T140000
DTEND;TZID=Europe/Stockholm:20210705T143000
UID:submissions.pasc-conference.org_PASC21_sess104_pap_dec106@linklings.co
 m
SUMMARY:Deploying Scientific AI Networks at Petaflop Scale on Secure Large
  Scale HPC Production Systems with Containers.
DESCRIPTION:Paper\n\nDeploying Scientific AI Networks at Petaflop Scale on
  Secure Large Scale HPC Production Systems with Containers.\n\nBrayford, A
 tanasov, Vallecorsa\n\nThe ever-increasing need for computational power to
  train complex models to tackle “real world” scientific proble
 ms, requires High Performance Computing (HPC) resources to efficiently com
 pute and scale complex models across thousands of nodes. In this paper, we
  discuss the issues associated with the deployment of AI frameworks on sec
 ure HPC systems and how we successfully deployed a modified machine learni
 ng framework on a secure large scale HPC production system, to train with 
 petascale performance a complex high energy physics 3D-GAN network. That w
 as used to reconstruct the energy pattern produced by showers of secondary
  particles inside a particle accelerator on various HPC systems.\n\nDomain
 : CS and Math, Life Sciences
END:VEVENT
END:VCALENDAR