Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs

Emmanuel Agullo 1, 2 Cédric Augonnet 2, 3 Jack Dongarra 4 Hatem Ltaief 4 Raymond Namyst 2, 3 Samuel Thibault 2, 3 Stanimire Tomov 4
1 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
3 RUNTIME - Efficient runtime systems for parallel architectures
Inria Bordeaux - Sud-Ouest, CNRS - Centre National de la Recherche Scientifique : UMR5800, UB - Université de Bordeaux
Abstract : In this chapter, we present a hybridization methodology for the development of linear algebra software for GPUs. The methodology is successfully used in MAGMA – a new generation of linear algebra libraries, similar in functionality to LAPACK, but extended for hybrid, GPU-based systems. Algorithms of interest are split into computational tasks. The tasks' execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators using StarPU – a runtime system for accelerator-based multicore architectures. StarPU enables to express parallelism through sequential-like code and schedules the different tasks over the hybrid processing units. The productivity becomes then fast and cheap as the development is high level, using existing software infrastructure. Moreover, the resulting hybrid algorithms are better performance-wise than corresponding homogeneous algorithms designed exclusively for either GPUs or homogeneous multicore CPUs.
Type de document :
Chapitre d'ouvrage
Wen-mei W. Hwu. GPU Computing Gems, 2, Morgan Kaufmann, 2010


https://hal.inria.fr/inria-00547847
Contributeur : Cédric Augonnet <>
Soumis le : vendredi 17 décembre 2010 - 15:06:36
Dernière modification le : jeudi 10 septembre 2015 - 01:08:30
Document(s) archivé(s) le : vendredi 18 mars 2011 - 02:47:54

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Emmanuel Agullo, Cédric Augonnet, Jack Dongarra, Hatem Ltaief, Raymond Namyst, et al.. Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs. Wen-mei W. Hwu. GPU Computing Gems, 2, Morgan Kaufmann, 2010. <inria-00547847>

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