Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling

Cheng-Hsiang Chiu, Chedi Morchdi, Yi Zhou, Boyang Zhang, Che Chang, Tsung-Wei Huang

Abstract: Dynamic task graph scheduling (DTGS) has become a powerful tool for parallel and heterogeneous applications, such as static timing analysis and large-scale machine learning. DTGS allows applications to define the task graph structure on-the-fly, enabling concurrent task creations and task executions. However, to schedule tasks, DTGS relies on applications to define a topological order for the task graph. Existing algorithms for generating this order primarily rely on heuristics like level-by-level sorting, which lack adaptability to dynamic computing environments. This paper proposes a novel method that leverages reinforcement learning to generate topological orders for DTGS systems. We will delve into the details of our design and present a real-world use case. For instance, when scheduling a large task graph with 3.9 million tasks and 7.4 million dependencies in a large-scale static timing analysis workload, our method achieves a speedup of up to 1.52 × compared to the baseline.