Computing least squares condition numbers on hybrid multicore/GPU systems
type: Dataset
description: This paper presents an efficient computation for least squares conditioning or estimates of it. We propose performance results using new routines on top of the multicore-GPU library MAGMA. This set of routines is based on an efficient computation of the variance-covariance matrix for which, to our knowledge, there is no implementation in current public domain libraries LAPACK and ScaLAPACK.
identifier: Report N°: RR-8479
issued: 2026-05-06T04:22:54.334120
language: en
modified: 2026-05-06T04:22:54.334124
contact point: Organization
  fn: CCSD
keyword: acm-g-mathematics-of-computingg1-numerical-analysisg13-numerical-linear-algebrag130-c
keyword: condition-number
keyword: gpu-computing
keyword: infoeu-reposemanticsreport
keyword: infoinfo-nacomputer-science-csnumerical-analysis-csna
keyword: linear-least-squares
keyword: magma-library
keyword: reports
keyword: statistical-condition-estimation
keyword: variance-covariance
landing page: https://inria.hal.science/hal-00947204

Computing least squares condition numbers on hybrid multicore/GPU systems
type: Distribution
format: HTML
issued: 2026-05-06T04:22:54.348974
modified: 2026-05-06T04:22:54.319601
access URL: https://inria.hal.science/hal-00947204

test_moissonnage_selune
type: Agent