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DTSTART;TZID=America/Denver:20231113T113000
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UID:submissions.supercomputing.org_SC23_sess447_ws_pmbss105@linklings.com
SUMMARY:Verifying Performance Guidelines for MPI Collectives at Scale
DESCRIPTION:Sascha Hunold (Technical University of Vienna)\n\nMPI collecti
 ve communication operations are crucial for high-performance computing, ma
 king the efficient implementation of collective algorithms essential for o
 ptimal application performance. While most MPI libraries provide several a
 lgorithms for a specific collective operation, each may work better in a s
 pecific scenario. Therefore, selecting the most suitable algorithm for eac
 h use case is important. However, even the best algorithm in a given MPI l
 ibrary’s set may deliver suboptimal performance.\n\nSelf-consistent MPI pe
 rformance guidelines are general expectations that collectives must meet t
 o be deemed performance-consistent. Specifically, a specialized collective
  call should not be slower than its less specialized counterparts. We intr
 oduce a tool for assessing the performance consistency of MPI collectives 
 in a statistically sound manner. Through a case study, we demonstrate the 
 current state of MPI performance consistency for three TOP500 machines.\n\
 nTag: Modeling and Simulation, Performance Measurement, Modeling, and Tool
 s\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Simon Ham
 mond (National Nuclear Security Administration (NNSA)); Stephen Jarvis (Un
 iversity of Birmingham, UK); and Steven A. Wright (University of York, Eng
 land)\n\n
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