A DAG partitioning-assisted list-based scheduler for homogeneous processors
Year: 2018
Type: preprint
Abstract: When scheduling a directed acyclic graph (DAG) of tasks on computational platforms, a good trade-off between load balance and data locality is necessary. List-based scheduling techniques, such as the earliest finish time (EFT) heuristic, are commonly used greedy approaches for this problem. The downside of EFT, and other 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 heuristics based on clustering for homogeneous platforms. Our approach uses an acyclic partitioner for DAGs for clustering. 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. We present an extensive experimental evaluation showing the trade-offs between the granularity of clustering and the parallelism, and how this affects the scheduling.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2898806590