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DTSTART;TZID=America/Chicago:20251118T153000
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UID:submissions.supercomputing.org_SC25_sess299_pap268@linklings.com
SUMMARY:Sparsified Preconditioned Conjugate Gradient Solver on GPUs
DESCRIPTION:Da Ma (McMaster University), Khalid Ahmad (University of Utah)
 , Kazem Cheshmi (McMaster University), and Hari Sundar and Mary Hall (Univ
 ersity of Utah)\n\nPreconditioned iterative sparse linear solvers are memo
 ry-efficient for large scientific simulations, but the dependences between
  iterations introduced by preconditioners limit parallelization. This issu
 e is exacerbated on GPUs, which feature many parallel cores. We propose a 
 sparsified preconditioned conjugate gradient (SPCG) solver that increases 
 parallelism by reducing dependences through sparsification, while preservi
 ng convergence behavior. We evaluate the proposed SPCG using both ILU(0) a
 nd ILU(K) preconditioners on a wide range of symmetric positive definite (
 SPD) matrices. The proposed SPCG improves the performance of the iterative
  phase of SPCG by a geometric mean speedup of 1.23$\times$ and 1.65$\times
 $ over the non-sparsified PCG using ILU(0) and ILU(K), respectively, on an
  NVIDIA A100 GPU. SPCG also yields geometric mean end-to-end speedups of 1
 .68$\times$ and 3.73$\times$ over the non-sparsified versions with ILU(0) 
 and ILU(K), respectively, on the same platform.\n\nTag: HPC for Machine Le
 arning, Performance Measurement, Modeling, & Tools, Programming Frameworks
 \n\nRecording: Livestreamed, Recorded\n\nRegistration Category: Technical 
 Program Reg Pass\n\nSession Chair: Abdel-Hameed A. Badawy (New Mexico Stat
 e University, Los Alamos National Laboratory (LANL))\n\n
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