An Efficient Technique of Application Mapping and Scheduling on Real-Time Multiprocessor Systems for Throughput Optimization

Multiprocessor systems are becoming ubiquitous in today's embedded systems design. In this article, we address the problem of mapping an application represented by a Homogeneous Synchronous Dataflow (HSDF) graph onto a real-time multiprocessor platform with the objective of maximizing total throughput. We propose that the optimal solution to the problem is composed of three components: actor-to-processor mapping, retiming, and actor ordering on each processor. The entire problem is systematically modeled into a Boolean Satisfiability (SAT) problem such that the optimal solution can be guaranteed theoretically. In order to explore the vast solution space more efficiently, we develop a specific HSDF theory solver based on the special characteristics of the timed HSDF, and integrate it into the general search framework of the SAT solver. Two alternative integration methods based on branch-and-bound are presented to achieve early branch pruning in the search space; thus, the scalability is greatly improved. Extensive performance evaluation on synthetic examples and a case study on the realistic H.264 Video Decoder show that our approach provides as much as 76.9% throughput improvement, and is scalable to industry-sized applications.