LOS: Level Order Sampling for Task Graph Scheduling on Heterogeneous Resources

Abstract

List scheduling is an approach to task graph scheduling that has been shown to work well for scheduling tasks with data dependencies on heterogeneous resources. Key to the performance of a list scheduling heuristic is its method to prioritize the tasks, and various ranking schemes have been proposed in the literature. We propose a method that combines multiple random rankings instead of a using a deterministic ranking scheme. We introduce L-Orders, which are a subset of all topological orders of a directed acyclic graph. L-Orders can be used to explore targeted regions of the space of all topological orders. Using the observation that the makespans in one such region are often approximately normal distributed, we estimate the expected time to solution improvement in certain regions of the search space. We combine targeted search and improvement time estimations into a time budgeted search algorithm that balances exploration and exploitation of the search space. In 40,500 experiments, our schedules are 5% shorter on average and up to 40% shorter in extreme cases than schedules produced by HEFT.