Accelerating Graph Transformer Models through GPU-Aware Kernel Fusion

Abstract

Graph Transformers have emerged as a powerful class of models that combine the representational flexibility of attention mechanisms with the relational structure of graphs. Their computational patterns, however, are poorly matched to conventional GPU execution strategies because they mix sparse neighborhood aggregation, dense attention, irregular memory access, and dynamic control flow. This paper examines GPU-aware kernel fusion as a system-level strategy for accelerating Graph Transformer models. Rather than treating fusion as a narrow compiler transformation, the discussion frames it as an architectural and infrastructural decision that shapes memory traffic, occupancy, synchronization, deployment economics, robustness, fairness, and sustainability. The paper first analyzes the workload characteristics of Graph Transformers and explains why unfused kernels waste bandwidth and launch overhead. It then evaluates fusion boundaries across message passing, attention, normalization, and feed-forward stages. The analysis considers trade-offs involving register pressure, shared-memory capacity, graph partitioning, batching, and distributed execution. It further situates kernel fusion within broader governance and policy contexts, including energy accounting, access equity, reproducibility, and operational resilience. Case illustrations from graph learning, large-scale GPU training, and attention optimization show that fusion can deliver substantial gains but also creates dependencies on hardware vendors, compiler maturity, and workload-specific tuning. The central argument is that GPU-aware kernel fusion is not merely an optimization technique but a socio-technical infrastructure choice whose benefits and risks must be evaluated together. The paper concludes with research directions for portable fusion, adaptive scheduling, sustainable deployment, and accountable performance reporting.