A Scalable Clustering-based Task Scheduler For Homogeneous Processors Using Dag Partitioning

General information

When scheduling a directed acyclic graph (DAG) of tasks with communication costs on computational platforms, a good trade-off between load balance and data locality is necessary. List-based scheduling techniques are commonly-used greedy approaches for this problem. The downside of list-scheduling heuristics is that they are incapable of making short-term sacrifices for the global efficiency of the schedule.

In this work, we describe three new list-based scheduling (meta-)heuristics based on clustering for homogeneous platforms, under the realistic duplex single-port communication model. Our approach uses an acyclic partitioner for DAGs for clustering (dagP, more on this here). The clustering enhances the data locality of the scheduler with a global view of the graph. Furthermore, since the partition is acyclic, we can schedule each part completely once its input tasks are ready to be executed.

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Latest release: dagPschedule (Latest 07/21/2020)


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