Tipo
Artículo de journal
Tipo de trabajo
1170–1182
Fecha
8
Accesion number
1532-0634
Edición
Concurrency and Computation: Practice and Experience
Sección
25
Volúmen
2013
Tertiary title
10.1002/cpe.2933
Abstract
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factorizations ({LU}, Cholesky, and {LDLT)} and the {Gauss–Jordan} method on hybrid platforms consisting of a multicore {CPU} and a many-core graphics processor ({GPU).} Specifically, we introduce the different matrix inversion algorithms by using a unified framework based on the notation from the {FLAME} project\ we develop hybrid implementations for those matrix operations underlying the algorithms, alternative to those in existing libraries for single {GPU} systems\
Autores
doi
benner_matrix_2013
Research Notes
and we perform an extensive experimental study on a platform equipped with state-of-the-art general-purpose architectures from Intel (Santa Clara, {CA}, {USA)} and a {‘Fermi’} {GPU} from {NVIDIA} (Santa Clara, {CA}, {USA)} that exposes the efficiency of the different inversion approaches. Our study and experimental results show the simplicity and performance advantage of the {Gauss–Jordan} elimination-based inversion methods and the difficulties associated with the symmetric indefinite case. Copyright © 2012 John Wiley & Sons, Ltd.
Keywords
1605710837