An Improved Parallel Scheduling Algorithm for Periodic Directed Acyclic Graphs

The periodic directed acyclic graph (DAG) is an important system model widely used to describe the structure and function of time-critical applications. The nodes in periodic DAGs are not only coupled with each other by input-output relations but also connected via the preceding period, making it hard to find an optimal schedule. This paper proposes an improved parallel scheduling algorithm for periodic DAGs (PSA-PDAG), decoupling the dependencies between nodes. In each period, PSA-PDAG computes more nodes in parallel, greatly improving the parallelism during computation. By applying PSA-PDAG, the computation time of each period is only the maximum update time among all nodes, which is superior to existing parallel algorithms. In typical periodic DAG examples, theoretical analysis and experimental results show that PSA-PDAG generally outperforms existing serial and hierarchical scheduling parallel algorithms. For instance, in the hybrid-structure large-scale experiment with 128 DAG nodes, compare with the 2.0x speedup of the hierarchical scheduling parallel algorithm, PSA-PDAG can achieve a considerable 48.6x speedup with 128 cores.