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DTSTART:19700308T020000
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DTSTAMP:20260522T150118Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181113T140000
DTEND;TZID=America/Chicago:20181113T143000
UID:submissions.supercomputing.org_SC18_sess209_pap186@linklings.com
SUMMARY:Energy Efficiency Modeling of Parallel Applications
DESCRIPTION:Mark Endrei, Chao Jin, Minh Ngoc Dinh, and David Abramson (Uni
 versity of Queensland); Heidi Poxon and Luiz DeRose (Cray Inc); and Bronis
  R. de Supinski (Lawrence Livermore National Laboratory)\n\nEnergy efficie
 ncy has become increasingly important in high performance computing (HPC),
  as power constraints and costs escalate. Workload and system characterist
 ics form a complex optimization search space in which optimal settings for
  energy efficiency and performance often diverge. Thus, we must identify t
 rade-off options to find the desired balance. We present an innovative sta
 tistical model that accurately predicts the Pareto optimal trade-off optio
 ns using only user-controllable parameters. Our approach can also tolerate
  both measurement and model errors. We study model training and validation
  using several HPC kernels, then with more complex workloads, including AM
 G and LAMMPS. We can calibrate an accurate model from as few as 12 runs, w
 ith prediction error of less than 10%. Our results identify trade-off opti
 ons allowing up to 40% energy efficiency improvement at the cost of under 
 20% performance loss. For AMG, we reduce the required sample measurement t
 ime from 13 hours to 74 minutes.\n\nTag: OpenMP, Performance, Power, Tools
 \n\nRegistration Category: Tech Program Reg Pass\n\nSession Chair: Felix W
 olf (Technical University of Darmstadt)\n\n
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