Quantization-Aware Acceleration of Graph Neural Networks on GPUs

Abstract

Graph neural networks have become a central computational model for relational data, spanning recommendation, chemistry, transportation, social analysis, and scientific discovery. Their deployment on graphics processing units has enabled substantial progress, yet graph workloads remain difficult to accelerate because they combine irregular memory access, dynamic neighbor sampling, sparse matrix operations, and dense neural computation. Quantization-aware acceleration offers a powerful but structurally complicated response to these pressures. By reducing numerical precision in representations, activations, messages, and gradients, quantization can lower memory bandwidth demand, increase arithmetic throughput, and make larger graphs tractable on fixed hardware. However, the benefits are not automatic. Quantized graph neural networks introduce new trade-offs among accuracy, robustness, fairness, compiler support, runtime scheduling, multi-GPU communication, and long-term infrastructure sustainability. This paper examines quantization-aware acceleration as a system-level problem rather than a purely algorithmic compression technique. It analyzes how low-precision arithmetic interacts with GPU kernel design, memory hierarchies, graph sampling, distributed training, and deployment governance. It also considers how quantization decisions affect vulnerable populations when graph models are used in high-stakes domains, and how documentation, auditing, and policy frameworks must adapt. The discussion concludes that effective quantization for graph neural networks requires co-design across hardware, compilers, runtime systems, model architectures, and institutional governance. Without such integration, quantization may deliver local efficiency gains while producing hidden costs in accuracy, inequity, and operational fragility.