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:20230124T171526Z
LOCATION:C1-2-3
DTSTART;TZID=America/Chicago:20221117T083000
DTEND;TZID=America/Chicago:20221117T170000
UID:submissions.supercomputing.org_SC22_sess275_rpost149@linklings.com
SUMMARY:PowerMan: Online Power Capping by Computationally Informed Machine
  Learning
DESCRIPTION:Posters, Research Posters\n\nPowerMan: Online Power Capping by
  Computationally Informed Machine Learning\n\nGebert, Barry, Dongarra\n\nA
 ppropriately adjusting the power draw of computational hardware plays a cr
 ucial role in its efficient use. While vendors have already implemented ha
 rdware-controlled power management, additional energy savings are availabl
 e, depending on the state of the machine. We propose the online classifica
 tion of such states based on computationally informed machine learning alg
 orithms to adjust the power cap of the next time step. This research highl
 ights that the overall energy consumption can be reduced significantly, of
 ten without a prohibitive penalty in the runtime of the applications.\n\nR
 egistration Category: Tech Program Reg Pass, Exhibits Reg Pass
END:VEVENT
END:VCALENDAR