Leveraging task-parallelism in message-passing dense matrix factorizations using SMPSs
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http://dx.doi.org/10.1016/j.parco.2014.04.001 |
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Título
Leveraging task-parallelism in message-passing dense matrix factorizations using SMPSsFecha de publicación
2014Editor
ElsevierISSN
0167-8191Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0167819114000441#?np=yPalabras clave / Materias
Resumen
In this paper, we investigate how to exploit task-parallelism during the execution of the
Cholesky factorization on clusters of multicore processors with the SMPSs programming
model. Our analysis reveals that the ... [+]
In this paper, we investigate how to exploit task-parallelism during the execution of the
Cholesky factorization on clusters of multicore processors with the SMPSs programming
model. Our analysis reveals that the major difficulties in adapting the code for this
operation in ScaLAPACK to SMPSs lie in algorithmic restrictions and the semantics of the
SMPSs programming model, but also that they both can be overcome with a limited
programming effort. The experimental results report considerable gains in performance
and scalability of the routine parallelized with SMPSs when compared with conventional
approaches to execute the original ScaLAPACK implementation in parallel as well as two
recent message-passing routines for this operation.
In summary, our study opens the door to the possibility of reusing message-passing
legacy codes/libraries for linear algebra, by introducing up-to-date techniques like dynamic
out-of-order scheduling that significantly upgrade their performance, while avoiding a
costly rewrite/reimplementation. [-]
Derechos de acceso
© 2014 Elsevier B.V. All rights reserved.
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