Data-aware Global Scheduling of Dataflow Process Networks

This work builds upon our model-based synthesis framework for the automatic generation of parallel software from dataflow process networks (DPNs). Our DPNs are described in the RVC-CAL language and our synthesis method employs a two-level scheduling scheme: At the first level, the global scheduler decides which actor executes next, and at the second level, the local scheduler determines which action of the scheduled actor executes next. The global scheduler works thereby in a round-robin fashion and selects the next idle actor without pending calls from the list of actors. In this paper, we propose a data-aware as-soon-as-possible (ASAP) scheduling scheme to modify the global scheduler with the aim to improve the end-to-end performance: First, a data-aware scheme is employed that prevents the global scheduler to redundantly test the source actors if the required data tokens for the desired computation are produced. Second, the scheduler is modified with an ASAP scheme that in contrast to a classical round-robin approach tests actors for scheduling in parallel. Based on our experimental evaluation, the proposed modifications demonstrated a speedup by a factor of up to 12 compared to the previous version.