Hierarchical Scheduling of an SDF/L Graph onto Multiple Processors

Abstract

Although dataflow models are known to thrive at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of data, represented well with loop structures, since these structures are not explicitly specified in existing dataflow models. SDF/L model overcomes this shortcoming by specifying the loop structures explicitly in a hierarchical fashion. We introduce a scheduling technique of an application represented by the SDF/L model onto heterogeneous processors. In the proposed method, we explore the mapping of tasks using an evolutionary meta-heuristic and schedule hierarchically in a bottom-up fashion, creating parallel loop schedules at lower levels first and then re-using them when constructing the schedule at a higher level. The efficiency of the proposed scheduling methodology is verified with benchmark examples and randomly generated SDF/L graphs.