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One Tier Dataflow Programming Model for Hybrid Distributed- and Shared-Memory Systems
HiPEAC 2016 Workshop on High-Level Parallel Programming for GPUs (HLPGPU)
Año del Documento
Universidad de Valladolid, Escuela de Ingeniería Informática
HiPEAC 2016 Workshop on High-Level Parallel Programming for GPUs (HLPGPU), Prague, Jan. 18-20 2016.
Dataflow programming consists in developing a program by describing its sequential stages and the interactions between them. The runtimes supporting this kind of programming are responsible of exploiting the parallelism by concurrently executing the different stages when their dependencies have been met. In this paper we introduce a new parallel programming model and framework based on the dataflow paradigm. Its features are: It is a unique one-tier model that supports hybrid shared- and distributed-memory systems; it can express activities arbitrarily linked, including cycles; it uses a distributed work-stealing mechanism to allow Multiple-Producer/Multiple-Consumer configurations; and it has a run-time mechanism for the reconfiguration of the dependences network which also allows to create task-to-task affinities. We present an evaluation using examples of different classes of applications. Experimental results show that programs generated using this framework deliver good performance, and that the new abstractions introduce minimal overheads.
MICINN (Spain) and ERDF program of the European Union: HomProg-HetSys project (TIN2014-58876-P), CAPAP-H5 network (TIN2014-53522-REDT), and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS), and by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETACIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain.
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Except where otherwise noted, this item's license is described as Attribution 4.0 International