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DTSTART:19700308T020000
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DTSTAMP:20250626T233528Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241120T100000
DTEND;TZID=America/New_York:20241120T170000
UID:submissions.supercomputing.org_SC24_sess533_post157@linklings.com
SUMMARY:Analyzing Alltoall Algorithms with SST
DESCRIPTION:Shannon Kinkead (Sandia National Laboratories) and Amanda Bien
 z (University of New Mexico)\n\nAlltoall collective operations in MPI are 
 critical in several types of computation, including matrix multiplication 
 and transposition, and machine learning applications. As a result, it is c
 ritical that these operations are performant for large amounts of data. Me
 anwhile, dragonfly networks are becoming more common in state-of-the-art s
 upercomputers. However, there has been little analysis of the performance 
 of alltoall operations on these networks. The hierarchical and modular nat
 ure of dragonfly networks results in distinct challenges in alltoall opera
 tions, though typical alltoall algorithms fail to account for topology. In
  this poster, we analyze the performance of the alltoall algorithm in four
  scenarios, and discuss the conditions under which each algorithm performs
  best.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\
 n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, Erlangen-
 Nuremberg; Erlangen National High Performance Computing Center); Sally Ell
 ingson (University of Kentucky); and Alan Sussman (University of Maryland)
 \n\n
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