Classification of General Data Flow Actors into Known Models of Computation
Classification of General Data Flow Actors into Known Models of Computation
复制标题
将通用数据流参与者分类为已知的计算模型
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
J. Teich
中科院分区：
文献类型：
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作者：
Christian Zebelein;J. Falk;C. Haubelt;J. Teich
由于不同的复杂性要求，信号处理领域中的应用通常由包含动态和静态数据流参与者的数据流图来建模。因此，为参与者建模所采用的表示法必须具有足够的表现力，以适应动态数据流参与者。另一方面，将静态数据流参与者视为动态数据流参与者会阻碍设计工具将特定于领域的优化方法应用于模型的静态部分，例如静态调度。在本文中，我们提出了一种通用符号和一种方法，将通过该符号表示的参与者分为同步和循环静态数据流计算模型。这使得能够使用统一的描述性语言来表达参与者的行为，同时仍然保留将特定于领域的优化方法应用于系统的各个部分的优势。在实验中，使用我们提出的自动分类和静态单处理器调度相结合的方法，我们可以将一般数据流图应用的延迟和吞吐量都提高57%。
Applications in the signal processing domain are often modeled by data flow graphs which contain both dynamic and static data flow actors due to heterogeneous complexity requirements. Thus, the adopted notation to model the actors must be expressive enough to accommodate dynamic data flow actors. On the other hand, treating static data flow actors like dynamic ones hinders design tools in applying domain-specific optimization methods to static parts of the model, e.g., static scheduling. In this paper, we present a general notation and a methodology to classify an actor expressed by means of this notation into the synchronous and cyclo-static dataflow models of computation. This enables the use of a unified descriptive language to express the behavior of actors while still retaining the advantage to apply domain-specific optimization methods to parts of the system. In experiments we could improve both latency and throughput of a general data flow graph application using our proposed automatic classification in combination with a static single-processor scheduling approach by 57%.