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UID:submissions.supercomputing.org_SC23_sess447_ws_pmbsf134@linklings.com
SUMMARY:Adaptive Stopping Rule for Performance Measurements
DESCRIPTION:Viyom Mittal, Pedro Bruel, Dejan Milojicic, and Eitan Frachten
 berg (Hewlett Packard Enterprise (HPE))\n\nPerformance variability in comp
 lex computer systems is a major challenge for accurate benchmarking and pe
 rformance characterization, especially for tightly-coupled large-scale hig
 h-performance computing systems. Point summaries of performance may be bot
 h uninformative, if they do not capture the full richness of its behavior,
  and inaccurate, if they are derived from an inadequate sample set of meas
 urements. Determining the correct sample size requires balancing tradeoffs
  of computation, methodology, and statistical power.\n\nWe treat the perfo
 rmance distribution as the primary target of the performance evaluation, f
 rom which all other metrics can be derived. We propose and evaluate a meta
 -heuristic that dynamically characterizes the performance distribution, de
 termining when enough samples have been collected to approximate the true 
 distribution. Compared to fixed stopping criteria, this adaptive method ca
 n be more efficient in resource use and more accurate. Importantly, it req
 uires no advance assumptions about the system under test or its performanc
 e characteristics.\n\nTag: Modeling and Simulation, Performance Measuremen
 t, Modeling, and Tools\n\nRegistration Category: Workshop Reg Pass\n\nSess
 ion Chairs: Simon Hammond (National Nuclear Security Administration (NNSA)
 ); Stephen Jarvis (University of Birmingham, UK); and Steven A. Wright (Un
 iversity of York, England)\n\n
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