Networks on-chip (NoCs) interconnect complex parallel applications on multiprocessors systems-on-chip. In order to rapidly evaluate NoCs, designers replace processing elements by communication traces on simulations. However, trace-based simulations have low accuracy due to the lack of information about packet dependence. The methods to obtain packet dependence require running multiple simulations or modifying application and simulator source code to output dependence; both are very costly. In this paper, we model packet dependence extraction as an association rule mining problem. We use the Apriori algorithm to extract communication patterns using traces from only one full-system simulation. The experiments with real, synthetic, and self-similar traffic show about 78% of packet dependence accuracy.

片上网络（NoC）互连多处理器片上系统上的复杂并行应用程序。为了快速评估NoC，设计人员通过模拟中的通信轨迹来替换处理元素。但是，由于缺少有关数据包依赖性的信息，因此基于跟踪的模拟的准确性较低。获得数据包依赖关系的方法需要运行多个仿真或修改应用程序和仿真器源代码以输出依赖关系。两者都非常昂贵。在本文中，我们将数据包依赖提取建模为关联规则挖掘问题。我们使用Apriori算法仅从一个完整的系统仿真中使用跟踪来提取通信模式。用真实，合成和自相似流量进行的实验显示出约78％的数据包依赖性准确性。