SCnC: Efficient Unification of Streaming with Dynamic Task Parallelism

Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelization. To take full advantage of the paradigm programmers are typically required to learn a new language and re-implement their applications. This work shows that it is possible to exploit streaming as a safe and automatic optimization of a more general dataflow-based model—one in which computation kernels are written in standard, general-purpose languages and organized as a coordination graph. We propose streaming concurrent collections (SCnC), a streaming system that can efficiently run a subset of programs supported by concurrent collections (CnC). CnC is a general purpose parallel programming paradigm that integrates task parallelism and dataflow computing. The proposed streaming support allows application developers to reason about their program as a general dataflow graph, while benefiting from the performance and tight memory footprint of stream parallelism when their program satisfies streaming constraints. In this paper, we formally define the application requirements for using SCnC, and outline a static decision procedure for identifying and processing eligible SCnC subgraphs. We present initial results showing that transitioning from general CnC to SCnC leads to a throughput increase of up to 40 × for certain benchmarks, and also enables programs with large data sizes to execute in available memory for cases where CnC execution may run out of memory.