A runtime approach to dynamic resource allocation for sparse direct solvers
HUGO, A.-E
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]
GUERMOUCHE, A
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
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]
WACRENIER, P.-A
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
See more >
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
HUGO, A.-E
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]
GUERMOUCHE, A
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
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]
WACRENIER, P.-A
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
NAMYST, R
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
< Reduce
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Language
en
Communication dans un congrès
This item was published in
International Conference on Parallel Processing (ICPP 2014), International Conference on Parallel Processing (ICPP 2014), 43rd International Conference on Parallel Processing, 2014-09-09, Minneapolis. 2014-09-09
English Abstract
—To face the advent of multicore processors and the ever increasing complexity of hardware architectures, pro-gramming models based on DAG-of-tasks parallelism regained popularity in the high performance, scientific computing ...Read more >
—To face the advent of multicore processors and the ever increasing complexity of hardware architectures, pro-gramming models based on DAG-of-tasks parallelism regained popularity in the high performance, scientific computing com-munity. In this context, enabling HPC applications to perform efficiently when dealing with graphs of parallel tasks that could potentially run simultaneously is a great challenge. Even if a uniform runtime system is used underneath, scheduling multiple parallel tasks over the same set of hardware resources introduces many issues, such as undesirable cache flushes or memory bus contention. In this paper, we show how runtime system-based scheduling contexts can be used to dynamically enforce locality of parallel tasks on multicore machines. We extend an existing generic sparse direct solver to use our mechanism and introduce a new decomposition method based on proportional mapping that is used to build the scheduling contexts. We propose a runtime-level dynamic context management policy to cope with the very irregular behavior of the application. A detailed performance analysis shows significant performance improvements of the solver over various multicore hardware.Read less <
Origin
Hal imported