Runtime Scheduling for Efficient Graph Neural Network Workloads on GPUs
Keywords:
graph neural networks; runtime scheduling; GPU systems; systems infrastructure; sustainable computing; fairness; governance; robustnessAbstract
Graph neural networks have become a central computational method for learning over relational, networked, and structured data, yet their execution on graphics processing units remains persistently difficult to optimize. Unlike dense tensor workloads, graph neural network computation is shaped by irregular adjacency, sparse feature access, dynamic batching, and highly variable neighborhood expansion. These properties make runtime scheduling a first-order systems concern rather than a secondary implementation detail. This paper examines runtime scheduling for efficient graph neural network workloads on graphics processing units from a system-level perspective. It analyzes the structural tension between latency-sensitive inference and throughput-oriented training, between memory capacity and compute utilization, and between centralized cluster governance and edge deployment. The discussion considers kernel design, multi-stream execution, memory management, graph partitioning, and service-level orchestration as interdependent layers. It further examines infrastructure implications for multi-tenant platforms, observability, reproducibility, and policy compliance. Sustainability and robustness are treated as design constraints, not post hoc concerns, because scheduling choices directly influence energy consumption, carbon intensity, failure recovery, and fairness across users and applications. The paper argues that future graph neural network systems require adaptive, topology-aware, and governance-aware runtimes that can balance performance with accountability. Such runtimes must coordinate hardware, software, and institutional policy to support efficient, resilient, and equitable graph learning at scale. The conclusion identifies research directions for scheduling abstractions that remain portable across hardware generations and deployment environments.
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