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Computing least squares condition numbers on hybrid multicore/GPU systems

Marc Baboulin 1, 2, * Jack Dongarra 3 Rémi Lacroix 4
* Corresponding author
1 POSTALE - Performance Optimization by Software Transformation and Algorithms & Librairies Enhancement
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
2 ParSys - LRI - Systèmes parallèles (LRI)
LRI - Laboratoire de Recherche en Informatique
4 ALPINES - Algorithms and parallel tools for integrated numerical simulations
LJLL - Laboratoire Jacques-Louis Lions, Inria Paris-Rocquencourt, INSMI - Institut National des Sciences Mathématiques et de leurs Interactions
Abstract : 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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Submitted on : Friday, February 28, 2014 - 7:51:14 PM
Last modification on : Friday, January 21, 2022 - 3:22:04 AM
Long-term archiving on: : Friday, May 30, 2014 - 3:46:05 PM


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  • HAL Id : hal-00947204, version 2


Marc Baboulin, Jack Dongarra, Rémi Lacroix. Computing least squares condition numbers on hybrid multicore/GPU systems. [Research Report] RR-8479, INRIA. 2014. ⟨hal-00947204v2⟩



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