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MPI-CUDA sparse matrix-vector multiplication for the conjugate gradient method with an approximate inverse preconditioner

Autor
Oyarzun, G.; Gorobets, A.; Oliva, A.; Borrell, R.
Tipus d'activitat
Article en revista
Revista
Computers and fluids
Data de publicació
2014-03-20
Volum
92
Pàgina inicial
244
Pàgina final
252
DOI
https://doi.org/10.1016/j.compfluid.2013.10.035 Obrir en finestra nova
Projecte finançador
DESARROLLO CODIGOS Y ALGORITMOS PARALELOS ALTA PRESTACIONES FINES DISEÑO OPTIMIZADO SISTEMAS Y EQUIPOS TERMICOS
Resum
The preconditioned conjugate gradient (PCG) is one of the most prominent iterative methods for the solution of sparse linear systems with symmetric and positive definite matrix that arise, for example, in the modeling of incompressible flows. The method relies on a set of basic linear algebra operations which determine the overall performance. Therefore, to achieve improvements in the performance, implementations of these basic operations must be adapted to the changes in the architecture of par...
Paraules clau
Approximate inverse preconditioner, Conjugate gradient, GPU, MPI-CUDA, Navier-stokes equation, Solvers, Sparse matrix vector multiplication
Grup de recerca
CTTC - Centre Tecnològic de la Transferència de Calor

Participants