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Actor Merging for Dataflow Process Networks

机译:数据流过程网络的Actor合并

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摘要

Dataflow process networks provide a versatile model of computation for specifying signal processing applications in a platform independent fashion. This attractive feature of dataflow has lately been realized in dataflow programming tools that allow synthesizing the same application specification as both fixed hardware circuits and as software for programmable processors. However, in practice, the specification granularity of the dataflow program remains an arbitrary choice of the designer. Dataflow specifications of the same application with equivalent I/O behaviour can range from a single dataflow actor to a very fine grained network composed of elementary processing operations. A very fine grained dataflow specification might result into a high performance implementation when synthesized as hardware, but might perform poorly when executed on a programmable processor. This article presents actor merging as one solution for this performance portability problem of dataflow programs. In contrast to previous work around actor merging, this article presents a methodology that can merge also dynamic dataflow actors. To support these claims, results of experiments on several processing platforms and application examples ranging from telecommunications to video compression are reported.
机译:数据流处理网络提供了通用的计算模型,用于以平台无关的方式指定信号处理应用程序。数据流的这种吸引人的特征最近已经在数据流编程工具中实现,该工具允许合成与固定硬件电路和可编程处理器软件相同的应用规范。但是,实际上,数据流程序的规范粒度仍然是设计人员的任意选择。具有相同I / O行为的同一应用程序的数据流规范,范围可以从单个数据流参与者到由基本处理操作组成的非常细粒度的网络。当作为硬件综合时,非常精细的数据流规范可能会导致高性能的实现,但是在可编程处理器上执行时可能会导致性能下降。本文介绍了Actor合并作为此数据流程序性能可移植性问题的一种解决方案。与先前有关参与者合并的工作相反,本文提出了一种可以合并动态数据流参与者的方法。为了支持这些主张,报告了在多个处理平台上的实验结果以及从电信到视频压缩的应用示例。

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