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DTSTAMP:20181221T160906Z
LOCATION:C145
DTSTART;TZID=America/Chicago:20181114T150000
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UID:submissions.supercomputing.org_SC18_sess468_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\nStudent Program, Tec
 h Program Reg Pass, ACM Student Research Competition\n\nStudying the Impac
 t of Power Capping on MapReduce-Based, Data-Intensive Mini-Applications on
  Intel KNL and KNM Architectures\n\nDavis\n\nIn this poster, we quantitati
 vely measure the impacts of data movement on performance in MapReduce-base
 d applications when executed on HPC systems. We leverage the PAPI ‘powerca
 p’ component to identify ideal conditions for execution of our application
 s in terms of (1) dataset characteristics (i.e., unique words); (2) HPC sy
 stem (i.e., KNL and KNM); and (3) implementation of the MapReduce programm
 ing model (i.e., with or without combiner optimizations). Results confirm 
 the high energy and runtime costs of data movement, and the benefits of th
 e combiner optimization on these costs.
URL:https://sc18.supercomputing.org/presentation/?id=spost114&sess=sess468
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