Improved Static Multiprocessor Scheduling using Cyclic Task Graphs: A Genetic Approach

Genetic algorithms have successfully been applied to multiprocessor task graph scheduling. In these previous attempts the schedules are constructed from directed acyclic task graphs. However, recurrent applications such as real-time control and digital signal processing can be expressed as directed cyclic task graphs. This paper presents a method for transforming a cyclic task graph into several alternate acyclic task graphs, The beneets of this are demonstrated through an example where the task graphs become easier to schedule through restructuring. Thus, the new task graphs result in better load balancing and less inter-processor traac than the \standard" acyclic task graphs. A genetic algorithm has been used to search for the task graph and schedule that best t a particular application and hardware connguration. The results are encouraging.