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:20230124T171521Z
LOCATION:D221
DTSTART;TZID=America/Chicago:20221113T104500
DTEND;TZID=America/Chicago:20221113T110000
UID:submissions.supercomputing.org_SC22_sess431_ws_sqcs101@linklings.com
SUMMARY:Position Paper: Challenges and Opportunities of Machine Learning f
 or Monitoring and Operational Data Analytics in Quantitative Codesign of S
 upercomputers
DESCRIPTION:Workshop\n\nPosition Paper: Challenges and Opportunities of Ma
 chine Learning for Monitoring and Operational Data Analytics in Quantitati
 ve Codesign of Supercomputers\n\nJakobsche, Ciorba, Lachiche\n\nThis work 
 examines the challenges and opportunities of using Machine Learning (ML) f
 or Monitoring and Operational Data Analytics (MODA) in the context of Quan
 titative Codesign of Supercomputers (QCS). MODA is employed to gain insigh
 ts into the behavior of current High Performance Computing (HPC) systems t
 o improve system efficiency, performance, and reliability (e.g. through op
 timizing cooling infrastructure, job scheduling, and application parameter
  tuning). In this work, we take the position that QCS in general, and MODA
  in particular, require close exchange with the ML community to realize th
 e full potential of data-driven analysis for the benefit of existing and f
 uture HPC systems. This exchange will facilitate identifying the appropria
 te ML methods to gain insights into current HPC systems and to go beyond e
 xpert-based knowledge and rules of thumb.\n\nSession Format: Recorded\n\nT
 ag: Architectures, Data Analytics, Datacenter, Extreme Scale Computing, HP
 C Community Collaboration, Machine Learning and Artificial Intelligence, P
 erformance, Resource Management and Scheduling, System Software\n\nRegistr
 ation Category: Workshop Reg Pass
END:VEVENT
END:VCALENDAR