A Proposal to Extend the OpenMP Tasking Model for Heterogeneous Architectures
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Otros documentos de la autoría: Ayguadé, Eduardo; Badía Sala, Rosa María; Cabrera, Daniel; Durán, Alejandro; González, Marc; Igual, Francisco; Jiménez González, Daniel; Labarta Mancho, Jesús; Martorell, Xavier; Mayo, Rafael; Pérez, Josep M.; Quintana-Orti, Enrique S.
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http://dx.doi.org/10.1007/978-3-642-02303-3_13 |
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Título
A Proposal to Extend the OpenMP Tasking Model for Heterogeneous ArchitecturesAutoría
Fecha de publicación
2009Editor
Springer Berlin HeidelbergISBN
978-3-642-02303-3Cita bibliográfica
AYGUADE, Eduard, et al. A proposal to extend the openmp tasking model for heterogeneous architectures. En: Evolving OpenMP in an Age of Extreme Parallelism: 5th International Workshop on OpenMP, IWOMP 2009 Dresden, Germany, June 3-5, 2009 Proceedings, p. 154-167. Springer Berlin Heidelberg, 2009. (Lecture Notes in Computer Science; 5568) ISBN 978-3-642-02303-3Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007/978-3-642-02303-3_13Palabras clave / Materias
Resumen
OpenMP has evolved recently towards expressing unstructured parallelism, targeting the parallelization of a broader range of applications in the current multicore era. Homogeneous multicore architectures from major ... [+]
OpenMP has evolved recently towards expressing unstructured parallelism, targeting the parallelization of a broader range of applications in the current multicore era. Homogeneous multicore architectures from major vendors have become mainstream, but with clear indications that a better performance/power ratio can be achieved using more specialized hardware (accelerators), such as SSE-based units or GPUs, clearly deviating from the easy-to-understand shared-memory homogeneous architectures. This paper investigates if OpenMP could still survive in this new scenario and proposes a possible way to extend the current specification to reasonably integrate heterogeneity while preserving simplicity and portability. The paper leverages on a previous proposal that extended tasking with dependencies. The runtime is in charge of data movement, tasks scheduling based on these data dependencies and the appropriate selection of the target accelerator depending on system configuration and resource availability. [-]
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