Pareto Optimal Scheduling for Synchronous Data Flow Graphs on Heterogeneous Multiprocessor

Streaming applications usually run on heterogeneous multiprocessor platforms and are required to have a high throughput, which in turn may increase the energy consumption. A trade-off between these two criteria is important for a system. Synchronous data flow graphs (SDFGs) are widely used to model streaming applications. In this paper, we propose a paralleled Pareto optimal scheduling method (PPOS) for SDFGs on heterogeneous multiprocessors. It deals with both time arrangement and processor allocation of computations. PPOS is an exact method to chart the Pareto space of energy consumption and throughput, and to find all Pareto optimal schedules of a system model. An approximation technique is presented to further increase the scalability of our methods. Our experiments are carried out on a practical multimedia application with different configurations and hundreds of synthesis graphs. The results show that the proposed methods are capable of dealing with large-scale models.