GPU Kernel Optimization for Scalable Graph Neural Network Training

Abstract

Graph neural networks have become a central computational method for learning over relational data, with applications ranging from recommendation and drug discovery to transportation, fraud detection, and scientific simulation. As these models scale to billions of edges and heterogeneous node types, the performance of training increasingly depends less on model expressiveness alone and more on the interaction between graph structure, memory hierarchy, parallel execution, and distributed infrastructure. This paper examines GPU kernel optimization as a system-level problem for scalable graph neural network training. It argues that kernel-level improvements cannot be understood in isolation from sampling strategy, data layout, cache behavior, communication scheduling, and governance constraints. The analysis considers irregular memory access, warp divergence, atomic contention, sparse tensor operations, and partitioning overhead as foundational bottlenecks. It then connects these bottlenecks to broader trade-offs involving throughput, latency, energy consumption, robustness, fairness, and deployment sustainability. The paper also discusses how infrastructure choices influence reproducibility, privacy, multi-tenancy, and policy compliance in production graph learning pipelines. Through cross-domain comparisons and forward-looking analysis, the paper positions GPU kernel optimization as a socio-technical design practice rather than a narrow implementation exercise. The conclusion emphasizes that scalable graph neural network training requires coordinated advances in hardware-aware algorithms, runtime systems, benchmarking standards, and governance frameworks. Such coordination is necessary for graph learning to remain efficient, equitable, and environmentally responsible as datasets and models continue to grow.