Integrated Task Clustering, Mapping and Scheduling for Heterogeneous Computing Systems
This paper presents a new approach for mapping and scheduling task graphs for heterogeneous hardware/software computing systems using heuristic search. Task mapping and scheduling are vital in hardware/software co-design and previous approaches that treat them separately lead to suboptimal solutions. In this paper, the authors propose two techniques to enhance the speedup of mapping/scheduling solutions: an integrated technique combining task clustering, mapping, and scheduling, and a multiple neighborhood function strategy. Their approach is demonstrated by case studies involving 40 randomly generated task graphs, as well as six applications.