调度感知同步数据流建模

对流应用系统进行吞吐量分析需要将周期静态顺序调度建模到数据流图中,吞吐量分析效率依赖于数据流图的规模及建模时间。为了提高吞吐量分析效率,提出基于同构同步数据流图的调度感知同步数据流模型及相应建模方法。通过利用应用模型结构特征及周期静态顺序调度,可减少模型中的任务、边和初始符号数目;可以使用已有分析方法对模型进行吞吐量分析。实验结果表明,所提建模方法优于已有方法,可有效提高吞吐量分析效率。

Modeling of schedule-aware synchronous dataflow

To analyze the throughput of streaming application systems, it is a necessity to model the periodic static order schedule into the synchronous dataflow graph, and the throughput analysis efficiency depends on the size of the dataflow graph and the modeling time. To improve the throughput analysis efficiency, a homogeneous synchronous dataflow graph based schedule-aware dataflow model and the modeling method are proposed. By exploiting the structure of the application model and periodic static order schedule, the task number, edge number and initial token number are reduced. Besides, the throughput of the model can be analyzed using available analyzing methods. Experimental results show that the proposed modeling method outperforms available methods, with the throughput analysis being speeded efficiently.