Grapheon RL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems
Aasish Kumar Sharma, Julian Martin Kunkel · 2025
Efficient workflow mapping and scheduling in heterogeneous HPC-Compute Continuum (HPC-CC) systems is critical for multi-objective optimization like optimizing resource utilization and minimizing makespan or energy efficiency. Existing approaches face fundamental trade-offs: Mixed-Integer Linear Programming (MILP) provides optimal solutions but becomes computationally intractable for large workflows exceeding (50x50) nodes by tasks, while heuristic methods sacrifice optimality for speed and struggle with complex constraint modeling. We present GrapheonRL, a novel Graph Neural Network (GNN) and Reinforcement Learning (RL)-based framework that can be embedded in Snakemake to model workflows as dependency-aware graphs, enabling RL agents to dynamically learn constraint-aware scheduling policies without mathematical reformulation. We evaluated GrapheonRL against MILP and heuristic baselines (HEFT, OLB) on Standard Task Graph Set workflows (11–90 tasks) and extended to synthetic workflows of up to (10,000x10,000) nodes by tasks, GrapheonRL matches MILP optimality while offering significantly improved scalability, achieving 76% faster inference with linear memory growth (0.87 MB per 1000 tasks). On complex workflows, GrapheonRL maintains optimal makespan (569) whereas heuristics degrade substantially (HEFT: 829, OLB: 1160), demonstrating that learning-based scheduling effectively bridges the optimality-scalability gap for dynamic HPC-CC environments.