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DTSTART;TZID=America/Denver:20231113T171000
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UID:submissions.supercomputing.org_SC23_sess453_ws_exampi103@linklings.com
SUMMARY:Efficient Data Redistribution for Malleable Applications
DESCRIPTION:Iker Martín Álvarez, José I. Aliaga, and Maribel Castillo (Uni
 versitat Jaume I) and Sergio Iserte (Barcelona Supercomputing Center (BSC)
 )\n\nProcess malleability can be defined as the ability of a distributed M
 PI parallel job to change the number of processes on--the--fly without sto
 pping its execution, reallocating the compute resources originally assigne
 d to the job, and without storing application data to disk.  MPI malleabil
 ity consists of four stages: resource reallocation, process management, da
 ta redistribution and execution resuming.  Among them, data redistribution
  is the most time-consuming and determines the reconfiguration time.\n\nIn
  this work, we compare different implementations of this stage using point
 -to-point and collective MPI operations, and discuss the impact of overlap
 ping computation-communication.  We then combine these strategies with dif
 ferent methods to expand/shrink jobs, using a synthetic application to emu
 late MPI-based codes and their malleable counterparts, in order to evaluat
 e the effect of different malleability methods in parallel distributed app
 lications.  The results show that the use of asynchronous techniques speed
 s up execution by 1.14 and 1.21, depending on the network used.\n\nTag: Ex
 ascale, Message Passing, Programming Frameworks and System Software\n\nReg
 istration Category: Workshop Reg Pass\n\nSession Chairs: Purushotham Banga
 lore (The University of Alabama); Amanda J. Bienz (University of New Mexic
 o); Matthew G. F. Dosanjh (Sandia National Laboratories); Ryan Grant (Quee
 n's University, Canada; Power API); William Schonbein (Sandia National Lab
 oratories); and Anthony Skjellum (Tennessee Technological University, ASCE
 ND-TNTECH)\n\n
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