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:20211207T054815Z
LOCATION:240-241-242
DTSTART;TZID=America/Chicago:20211118T163000
DTEND;TZID=America/Chicago:20211118T170000
UID:submissions.supercomputing.org_SC21_sess166_pap594@linklings.com
SUMMARY:Characterization and Prediction of Deep Learning Workloads in Larg
 e-Scale GPU Datacenters
DESCRIPTION:Paper\n\nCharacterization and Prediction of Deep Learning Work
 loads in Large-Scale GPU Datacenters\n\nHu, Sun, Yan, Wen, Zhang\n\nModern
  GPU datacenters are critical for delivering Deep Learning (DL) models and
  services in both the research community and industry. When operating a da
 tacenter, optimization of resource scheduling and management can bring sig
 nificant financial benefits. Achieving this goal requires a deep understan
 ding of the job features and user behaviors. We present a comprehensive st
 udy about the characteristics of DL jobs and resource management. First, w
 e perform a large-scale analysis of real-world job traces from SenseTime. 
 We uncover some interesting conclusions from the perspectives of clusters,
  jobs and users, which can facilitate the cluster system designs. Second, 
 we introduce a general-purpose framework, which manages resources based on
  historical data. As case studies, we design: a Quasi-Shortest-Service-Fir
 st scheduling service, which can minimize the cluster-wide average job com
 pletion time by up to 6.5x; and a Cluster Energy Saving service, which imp
 roves overall cluster utilization by up to 13%.\n\nTag: Reproducibility Ba
 dge, Applications, Big Data, Datacenter, File Systems and I/O, Machine Lea
 rning and Artificial Intelligence, State of the Practice, Storage\n\nRegis
 tration Category: Tech Program Reg Pass\n\nReproducibility Badges: Artifac
 t Available, Artifact Functional, Results Reproduced
END:VEVENT
END:VCALENDAR