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DTSTART;TZID=America/Chicago:20251116T161000
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UID:submissions.supercomputing.org_SC25_sess196_ws_pawatm105@linklings.com
SUMMARY:KDRSolvers: Scalable, Flexible, Task-Oriented Krylov Solvers
DESCRIPTION:David Kai Zhang, Rohan Yadav, Alex Aiken, and Fredrik Kjolstad
  (Stanford University) and Sean Treichler (NVIDIA Corporation)\n\nWe prese
 nt KDRSolvers, a novel framework for representing sparse linear systems an
 d implementing Krylov subspace methods on modern heterogeneous supercomput
 ers. KDRSolvers uses dependent partitioning to uniformly represent sparse 
 matrix storage formats as abstract maps between a matrix's domain, range, 
 and set of nonzero entries. This enables KDRSolvers to define universal co
 -partitioning operators for matrices and vectors independent of underlying
  storage formats, allowing changes in data partitioning strategies to auto
 matically propagate through an application with no code modification. KDRS
 olvers also introduces multi-operator systems in which matrix and vector d
 ata can be ingested and processed in multiple non-contiguous pieces withou
 t data movement. Our implementation of KDRSolvers, targeting the Legion ru
 ntime system, achieves greater flexibility and competitive performance com
 pared to PETSc and Trilinos. In experiments with up to 1,024 GPUs on the L
 assen supercomputer, our implementation achieves up to a 9.6% reduction in
  execution time per iteration.\n\nRecording: Livestreamed, Recorded\n\nReg
 istration Category: Technical Program Reg Pass, Workshop Reg Pass\n\nSessi
 on Chairs: Engin Kayraklioglu (Hewlett Packard Enterprise (HPE)), Karla Va
 nessa Morris Wright (Sandia National Laboratories), Kenjiro Taura (The Uni
 versity of Tokyo), Daniele Lezzi (Barcelona Supercomputing Center (BSC)), 
 Irene Moulitsas (Cranfield University), Katherine Rasmussen (Lawrence Berk
 eley National Laboratory (LBNL)), and Elliott Slaughter (SLAC National Acc
 elerator Laboratory)\n\n
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