Composing multiple StarPU applications over heterogeneous machines: A supervised approach
HUGO, Andra
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]
GUERMOUCHE, Abdou
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, Pierre-André
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Leer más >
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
HUGO, Andra
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]
GUERMOUCHE, Abdou
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, Pierre-André
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, Raymond
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
< Leer menos
Efficient runtime systems for parallel architectures [RUNTIME]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Idioma
en
Article de revue
Este ítem está publicado en
International Journal of High Performance Computing Applications. 2014-02, vol. 28, p. 285 - 300
SAGE Publications
Resumen en inglés
Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great chal-lenge. Even if a uniform runtime system is used underneath, scheduling tasks or threads coming from ...Leer más >
Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great chal-lenge. Even if a uniform runtime system is used underneath, scheduling tasks or threads coming from different libraries over the same set of hardware resources introduces many issues, such as resource oversubscription, undesirable cache flushes and memory bus contention. This paper presents an extension of StarPU, a runtime system specifically designed for heterogeneous architectures, that allows multiple parallel codes to run concurrently with minimal interference. Such parallel codes run within schedul-ing contexts that provide confined execution environments which can be used to partition computing resources. Scheduling contexts can be dynamically resized to optimize the allocation of computing resources among concurrently running libraries. We introduce a hypervisor that automatically expands or shrinks contexts using feedback from the run-time system (e.g. resource utilization). We demonstrate the relevance of our approach using benchmarks invoking multi-ple high performance linear algebra kernels simultaneously on top of heterogeneous multicore machines. We show that our mechanism can dramatically improve the overall application run time (-34%), most notably by reducing the average cache miss ratio (-50%).< Leer menos
Palabras clave en inglés
resource allocation
runtime optimisation
Parallel composition
heterogeneous architectures
scheduling
Orígen
Importado de HalCentros de investigación