One Tier Dataflow Programming Model for Hybrid Distributed- and Shared-Memory Systems

Abstract

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.