Computing least squares condition numbers on hybrid multicore/GPU systems

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.

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Source https://inria.hal.science/hal-00947204
Author Baboulin, Marc, Dongarra, Jack, Lacroix, Rémi
Maintainer CCSD
Last Updated May 6, 2026, 04:22 (UTC)
Created May 6, 2026, 04:22 (UTC)
Identifier Report N°: RR-8479
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Performance Optimization by Software Transformation and Algorithms & Librairies Enhancement (POSTALE) ; Laboratoire de Recherche en Informatique (LRI) ; Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Saclay ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
creator Baboulin, Marc
date 2014-02-14T00:00:00
harvest_object_id 49bd6be0-8d6e-4747-b831-17d2c9a57b4e
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-10-24T00:00:00
set_spec type:REPORT