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:20211207T055338Z
LOCATION:230-231-232
DTSTART;TZID=America/Chicago:20211119T084000
DTEND;TZID=America/Chicago:20211119T091000
UID:submissions.supercomputing.org_SC21_sess330_misc415@linklings.com
SUMMARY:On The Experience of Long Time Data Collection on SuperMUC to Driv
 e Energy Efficiency
DESCRIPTION:Workshop\n\nOn The Experience of Long Time Data Collection on 
 SuperMUC to Drive Energy Efficiency\n\nSchulz\n\nOn our path to exascale, 
 HPC systems are starting to push the limits in energy and power consumptio
 n that are technically, economically and politically feasible. Therefore, 
 increasing energy efficiency is one of the most important design goals of 
 HPC architectures and a central goal for most HPC System Co-Design efforts
 . However, energy efficiency can only be achieved – and later evaluated – 
 if we first understand the energy and power consumption in both current an
 d past architectures and their applications. For the SuperMUC series of HP
 C systems at the Leibniz Supercomputing Centre in Garching, Germany, we th
 erefore implemented a comprehensive monitoring system (the Data Center Dat
 a Base and the PerSyst framework) as well as a matching analytics engine (
 Wintermute) that enables us, together with a comprehensive system instrume
 ntation, to long-term track the energy efficiency of all SuperMUC systems.
  In this talk, I will discuss our infrastructure, the data collected, the 
 insights we were able to gain, and how we were able to use the data to aff
 ect machine design and operation.\n\nTag: Architectures, Data Analytics, D
 atacenter, Emerging Technologies, Extreme Scale Computing, Heterogeneous S
 ystems, HPC Community Collaboration, Machine Learning and Artificial Intel
 ligence, Performance, Resource Management and Scheduling, System Administr
 ation, System Software and Runtime Systems\n\nRegistration Category: Works
 hop Reg Pass
END:VEVENT
END:VCALENDAR