BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260522T150118Z
LOCATION:D173
DTSTART;TZID=America/Chicago:20181114T133000
DTEND;TZID=America/Chicago:20181114T140000
UID:submissions.supercomputing.org_SC18_sess276_exforum135@linklings.com
SUMMARY:Managing the Convergence of HPC and AI
DESCRIPTION:J.J. Falkanger (Lenovo)\n\nHPC environments are the most signi
 ficant source of processing capacity in many organizations, and more users
  want to leverage the power of the “SuperComputer" for their workloads to 
 get performance beyond the single box.  These "new customers" for your HPC
  cluster may have little knowledge on how to access, configure and deploy 
 workloads where typical open-source cluster management solutions are used,
  driving a significant amount of handholding for administrators.  In parti
 cular, users wanting to experiment with or deploy AI training on the clust
 er may simply have data and a desire to train, without the technical exper
 tise to configure scripts, resources, frameworks, libraries, etc. to run. 
 Bringing these new users into the HPC environment is a significant opportu
 nity to grow and expand the value of your infrastructure – but only if it 
 is easy to use and easy to manage, and consistent for both HPC and AI work
 loads.\n\nTag: Data Management, Machine Learning, Containers\n\nSession Ch
 air: Preston Smith (Purdue University)\n\n
END:VEVENT
END:VCALENDAR