DAG Scheduling Considering Parallel Execution for High-Load Processing on Clustered Many-core Processors

In recent years, high computational power has been required for computer platforms to support complex systems such as self-driving systems. Clustered many-core processors and directed acyclic graphs (DAGs), which can represent dependencies and parallelism of task processing, have attracted much attention as solutions to this problem. Previous studies on scheduling DAGs on multi-core processors have attempted to reduce the makespan (i.e., time it takes for a task to complete) by increasing the number of processes that can be executed in parallel. However, in self-driving systems, such as those utilizing clustered many-core processors, it is impossible to sufficiently increase the utilization of processor cores due to high-load processing. In this paper, a scheduling method is proposed to improve the utilization of processor cores by parallel executing high-load processes in parallel across multiple cores. The proposed method can reduce the makespan of DAGs performing high-load processing on clustered many-core processors.