Hardware-Aware Graph Neural Network Acceleration on Modern GPUs
Keywords:
graph neural networks; GPU acceleration; hardware-aware computing; graph systems; distributed training; memory hierarchy; sustainability; fairness; policyAbstract
Graph neural networks have become a central computational method for learning over relational data, with applications spanning recommendation, molecular modeling, fraud detection, transportation, and scientific simulation. Although modern graphics processing units offer substantial arithmetic throughput and memory bandwidth, graph neural network workloads remain difficult to accelerate because their execution is dominated by irregular memory access, sparse communication, dynamic topology, and variable-degree aggregation. This paper examines hardware-aware graph neural network acceleration as a socio-technical systems problem rather than a narrow kernel optimization problem. It analyzes the structural trade-offs among graph representation, memory hierarchy use, kernel specialization, multi-GPU distribution, compiler scheduling, and deployment governance. The discussion emphasizes that acceleration choices influence not only latency and throughput but also reproducibility, robustness, fairness, energy sustainability, and policy accountability. Through cross-domain comparisons and infrastructure-level analysis, the paper argues that effective acceleration requires co-design across algorithms, hardware, runtime systems, and institutional practices. It concludes that the next generation of graph learning systems must treat efficiency, equity, and environmental responsibility as inseparable design constraints, especially as graph models move from research prototypes to critical infrastructure.
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