Computing least squares condition numbers on hybrid multicore/GPU systems

M. Baboulin, J. Dongarra, R. Lacroix

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This chapter 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.

Original languageEnglish
Title of host publicationInterdisciplinary Topics in Applied Mathematics, Modeling and Computational Science
EditorsRoman N. Makarov, Roderick V. N. Melnik, Ilias S. Kotsireas, Hasan Shodiev, Monica G. Cojocaru, Monica G. Cojocaru, Roman N. Makarov, Roderick V. N. Melnik, Ilias S. Kotsireas, Hasan Shodiev
PublisherSpringer New York LLC
Pages35-41
Number of pages7
ISBN (Print)9783319123066, 9783319123066
DOIs
StatePublished - 2015
Externally publishedYes
EventInternational Conference on Applied Mathematics, Modelling and Computational Science, AMMCS 2013 - Waterloo, Canada
Duration: Aug 26 2013Aug 30 2013

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume117
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

ConferenceInternational Conference on Applied Mathematics, Modelling and Computational Science, AMMCS 2013
Country/TerritoryCanada
CityWaterloo
Period08/26/1308/30/13

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