A dependency-aware task-based programming environment for multi-core architectures

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/9944

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.contributor.author	Pérez Cáncer, Josep Maria
dc.contributor.author	Badia Sala, Rosa Maria
dc.contributor.author	Labarta Mancho, Jesús José
dc.date	2008
dc.identifier.citation	Pérez, J.; Badia, R.; Labarta, J. A dependency-aware task-based programming environment for multi-core architectures. A: IEEE International Conference on Cluster Computing. "2008 IEEE International Conference on Cluster Computing". Tsukuba: 2008, p. 142-151.
dc.identifier.citation	978-1-4244-2640-9
dc.identifier.citation	10.1109/CLUSTR.2008.4663765
dc.identifier.uri	http://hdl.handle.net/2117/9944
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subject	Multiprocessing systems
dc.subject	Parallel architectures
dc.subject	Programming environments
dc.subject	Software libraries
dc.subject	Entorns de programació
dc.subject	Arquitectura de computadors
dc.title	A dependency-aware task-based programming environment for multi-core architectures
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	Parallel programming on SMP and multi-core architectures is hard. In this paper we present a programming model for those environments based on automatic function level parallelism that strives to be easy, flexible, portable, and performant. Its main trait is its ability to exploit task level parallelism by analyzing task dependencies at run time. We present the programming environment in the context of algorithms from several domains and pinpoint its benefits compared to other approaches. We discuss its execution model and its scheduler. Finally we analyze its performance and demonstrate that it offers reasonable performance without tuning, and that it can rival highly tuned libraries with minimal tuning effort.
dc.description.abstract	Peer Reviewed

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