Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs
AGULLO, Emmanuel
High-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
High-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
AUGONNET, Cédric
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
See more >
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
AGULLO, Emmanuel
High-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
High-End Parallel Algorithms for Challenging Numerical Simulations [HiePACS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
AUGONNET, Cédric
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
NAMYST, Raymond
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
THIBAULT, Samuel
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
< Reduce
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
Language
en
Chapitre d'ouvrage
This item was published in
GPU Computing Gems, GPU Computing Gems. 2010-09, vol. 2
Morgan Kaufmann
English 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 ...Read more >
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.Read less <
European Project
Performance Portability and Programmability for Heterogeneous Many-core Architectures
ANR Project
Programmation des technologies multicoeurs hétérogènes - ANR-08-COSI-0013
Origin
Hal imported