Abstract
The problem of scheduling Directed Acyclic Graphs in order to minimizemakespan(schedule length), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presentsHMDS-Bl, a list-based heuristicmakespanminimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently,HMDS-Blhas been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm calledHMDS.HMDShas been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance (makespan) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown thatHMDSis able to comprehensively outperform state-of-the-art algorithms such asHEFT,PEFT,PPTS, etc., in terms of archivedmakespanswhile incurring bounded additional computation time overhead.
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