A class of communication-avoiding algorithms for solving general dense linear systems on CPU/GPU parallel machines
type: Dataset
description: We study several solvers for the solution of general linear systems where the main objective is to reduce the communication overhead due to pivoting. We first describe two existing algorithms for the LU factorization on hybrid CPU/GPU architectures. The first one is based on partial pivoting and the second uses a random preconditioning of the original matrix to avoid pivoting. Then we introduce a solver where the panel factorization is performed using a communication-avoiding pivoting heuristic while the update of the trailing submatrix is performed by the GPU. We provide performance comparisons for these solvers on current hybrid multicore-GPU parallel machines.
landing page: https://inria.hal.science/hal-00656457
identifier: Report N°: RR-7854
keyword: communication-avoiding-algorithms
keyword: dense-linear-algebra-libraries
keyword: hybrid-multicoregpu-computing
keyword: infoeu-reposemanticsreport
keyword: infoinfo-nacomputer-science-csnumerical-analysis-csna
keyword: linear-system-solvers
keyword: lu-factorization
keyword: reports
issued: 2026-05-26T09:41:20.954292
modified: 2026-05-26T09:41:20.954300
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

A class of communication-avoiding algorithms for solving general dense linear systems on CPU/GPU parallel machines
type: Distribution
format: HTML
access URL: https://inria.hal.science/hal-00656457
issued: 2026-05-26T09:41:20.971902
modified: 2026-05-26T09:41:20.905611