DAG Scheduling and Analysis on Multiprocessor Systems: Exploitation of Parallelism and Dependency

A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network

“…The GoSu model consistently shows competitive performance compared to several state-of-the-art DAG scheduling heuristic methods based on fixed-priority assignments (i.e., [1], [2]) under various experimental settings. Furthermore, the model often achieves better performance than those heuristic methods particularly in nontrivial task configurations, e.g., showing a 2∼3% gain in the slowdown of achieved makespans for the cases when the number of processors is 3 or 4 with moderate parallelism and when the number of processors is between 3 and 8 with high parallelism (Figure 4(b) and (c)).…”

“…A directed acyclic graph (DAG) task has been used to represent the dependencies among a number of task components (subtasks) and to formulate a fine-grained parallel scheduling problem with the interdependent subtasks. Furthermore, as non-preemptive task models can avoid the overhead issue of migration and switching tasks, recently, priority-based non-preemptive scheduling for a DAG task has gained much attention [1]. The problem has been tackled by investigating scheduler techniques with priority assignments, which take a set of subtasks in a single DAG as input and produce a priority order for the subtasks and their non-deterministic execution order [1], [2].…”

“…Furthermore, as non-preemptive task models can avoid the overhead issue of migration and switching tasks, recently, priority-based non-preemptive scheduling for a DAG task has gained much attention [1]. The problem has been tackled by investigating scheduler techniques with priority assignments, which take a set of subtasks in a single DAG as input and produce a priority order for the subtasks and their non-deterministic execution order [1], [2].…”

“…Given a priority order, subtasks can be scheduled to run in parallel on multiple processors subject to the precedence conditions. Existing studies have developed heuristic priority assignment algorithms and analyzed their impact on the goal of minimizing the elapsed time (or makespan) required to complete all the subtasks [1]- [3], as there might be a number of different priority orders, e.g., up to n! where n is the number of subtasks.…”

Brief Industry Paper: Digital Twin for Dependable Multi-Core Real-Time Systems — Requirements and Open Challenges

“…Success relies on components using models at the right level of abstraction and being able to capture the dependencies of functional software components, for example, by modelling parallel tasks as Directed Acyclic Graphs (DAGs) [1]. The models would then enable the evaluation of feasibility and schedulability.…”