Multi-GPU Parallelization Strategies for Large-Scale Graph Learning
Keywords:
multi-GPU systems, graph neural networks, parallelization, distributed training, graph partitioning, memory hierarchy, sustainability, infrastructure governanceAbstract
Large-scale graph learning has become a central computational workload in artificial intelligence because graphs can represent social networks, transportation systems, biological pathways, supply chains, financial transactions, and scientific simulations. As graph sizes and model depths increase, single-device training rapidly becomes impractical, making multi-GPU parallelism a necessary but structurally difficult design space. This paper examines multi-GPU parallelization strategies for large-scale graph learning from a systems perspective, emphasizing architectural trade-offs, data movement, memory hierarchy, scheduling, robustness, fairness, sustainability, deployment, and governance. The analysis distinguishes between graph partitioning, feature partitioning, model parallelism, pipeline execution, tensor parallel decomposition, and hybrid scheduling, showing that each strategy redistributes bottlenecks rather than eliminating them. It argues that the most consequential decisions are not merely about scaling throughput but about aligning graph topology, communication topology, memory capacity, and organizational incentives. The paper also considers failure management, reproducibility, energy consumption, and equitable access to infrastructure. Through cross-domain comparisons with recommender systems, scientific computing, and language model training, it identifies persistent tensions among locality, load balance, staleness, and scalability. The conclusion suggests that future multi-GPU graph learning systems must treat parallelism as a socio-technical design problem, not only a kernel optimization problem. Robust progress will depend on co-designed algorithms, hardware-aware schedulers, transparent benchmarking, and governance frameworks that make large-scale graph learning both efficient and accountable.
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