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DTSTART:19700308T020000
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DTSTAMP:20181221T160904Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess325_spost114@linklings.com
SUMMARY:Studying the Impact of Power Capping on MapReduce-Based, Data-Inte
 nsive Mini-Applications on Intel KNL and KNM Architectures
DESCRIPTION:ACM Student Research Competition, Poster\nTech Program Reg Pas
 s, Exhibits Reg Pass\n\nStudying the Impact of Power Capping on MapReduce-
 Based, Data-Intensive Mini-Applications on Intel KNL and KNM Architectures
 \n\nDavis\n\nIn this poster, we quantitatively measure the impacts of data
  movement on performance in MapReduce-based applications when executed on 
 HPC systems. We leverage the PAPI ‘powercap’ component to identify ideal c
 onditions for execution of our applications in terms of (1) dataset charac
 teristics (i.e., unique words); (2) HPC system (i.e., KNL and KNM); and (3
 ) implementation of the MapReduce programming model (i.e., with or without
  combiner optimizations). Results confirm the high energy and runtime cost
 s of data movement, and the benefits of the combiner optimization on these
  costs.
URL:https://sc18.supercomputing.org/presentation/?id=spost114&sess=sess325
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