Modeling adaptive streaming applications with parameterized polyhedral process networks

Abstract

The Kahn Process Network (KPN) model is a widely used model-of-computation to specify and map streaming applications onto multiprocessor systems-on-chips. In general, KPNs are difficult to analyze at design-time. Thus a special case of the KPN model, called Polyhedral Process Networks (PPN), has been proposed to address the analyzability issue. However, the PPN model is not able to capture adaptive/dynamic behavior. Such behavior is usually expressed by using parameters which values are reconfigured at run-time. To model the adaptive/dynamic applications, in this paper we introduce an extension of the PPN model, called Parameterized Polyhedral Process Networks (P3N), which still provides design-time analyzability to some extent. We first formally define the P3N model and its operational semantics. In addition, we devise a design-time analysis to extract relations between parameters. Based on the analysis, we propose an approach to ensure that consistent execution of the P3N model is preserved at run-time. Using an FPGA-based MPSoC platform, we present a performance evaluation of the possible overhead caused by the run-time reconfiguration.