Memory-Efficient GNN Training via Adaptive Graph Data Layouts
Keywords:
graph neural networks, adaptive data layouts, memory efficiency, distributed training, systems architecture, sustainability, governance, graph samplingAbstract
Graph neural networks have become a central computational method for learning over relational data, yet their training remains constrained by irregular memory access, adjacency storage expansion, and poor locality across accelerators and distributed systems. This paper examines memory-efficient graph neural network training through the lens of adaptive graph data layouts. Rather than treating layout as a static preprocessing decision, the paper positions adaptive layouts as a systems abstraction that couples graph structure, hardware topology, runtime scheduling, and governance concerns. It analyzes how vertex-centric, edge-centric, blocked, tiled, cache-aware, and hybrid representations interact with memory hierarchies, feature stores, sampling engines, and communication substrates. The discussion emphasizes structural trade-offs among preprocessing cost, layout mutation overhead, load balance, bandwidth utilization, fault tolerance, and reproducibility. It then extends the analysis to distributed and heterogeneous infrastructures, where partitioning, replication, caching, and compression determine whether training can scale without excessive synchronization or energy consumption. The paper also considers robustness, fairness, and policy implications, arguing that adaptive layouts are not merely performance artifacts but infrastructural choices that shape access, accountability, and sustainability. Case illustrations from graph sampling systems, kernel-level acceleration, and large-scale deployment environments show that memory efficiency depends on coordinated design across algorithms, compilers, storage, and operations. The conclusion identifies open challenges in portable adaptive layouts, benchmark standardization, carbon-aware scheduling, and equitable deployment. The central claim is that adaptive graph data layouts offer a viable path toward memory-efficient graph neural network training only when they are treated as governed socio-technical infrastructure rather than isolated optimization tricks.
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