Graph Neural Networks (GNNs) have achieved remarkable success across domains such as recommendation and scientific computing, yet their practical deployment remains constrained by high execution cost. The diversity of GNN model structures and the sparsity of real-world graphs pose two fundamental challenges for hardware acceleration: supporting heterogeneous operator patterns and achieving high resource utilization under irregular data access. Existing accelerators often address only one aspect, either targeting specific models with hardwired pipelines or applying general architectures with limited efficiency. To address these challenges, we propose SWITCHBLADE, a full-stack framework for GNN acceleration through the coordinated design of partitioning, compilation, and architecture. SWITCHBLADE addresses these challenges through three key components. First, a phase-based intermediate representation unifies diverse GNN models by abstracting computation stages for model-independent code generation. Second, a fine-grained graph partitioner enhances data locality and reduces memory traffic by adapting to graph topology and model semantics. Third, the hardware architecture supports stream-level parallelism and decoupled execution to exploit cross-shard and inter-phase concurrency. Evaluation on representative models and datasets shows that SWITCHBLADE achieves up to 1.85× speedup and 19.03× energy savings over an NVIDIA V100 GPU, while outperforming state-of-the-art GNN accelerators across diverse full-graph workloads, demonstrating both high efficiency and broad model generality.

Graph neural networks (GNNs) are gaining popularity in diverse application domains and growing in complexity.As a result, it is crucial to achieve high-performance GNN execution.Among various techniques, algebraic transformations, including operator reordering and operator fusion, have been successfully applied to improve the computation and memory access efficiencies of DNN models.However,

As graph neural networks (GNNs) have achieved great success in many graph learning problems, it is of paramount importance to support their efficient execution. Different graphs and different operators present different patterns during execution. However, there is still a gap in the existing GNN acceleration research to explore adaptive parallelism. We show that existing GNN frameworks rely on handwritten static kernels, which fail to achieve the best performance across different graph operators and input graph structures. In this work, we propose uGrapher, a unified interface that achieves general high performance for different graph operators and datasets. The existing GNN frameworks can easily integrate our design for its simple and unified API. We take a principled approach that decouples a graph operator’s computation and schedule to achieve that. We first build a GNN-specific operator abstraction that incorporates the semantics of graph tensors and graph loops. We explore various schedule strategies based on the abstraction that can balance the well-established trade-off relationship between parallelism, locality, and efficiency. Our evaluation shows that uGrapher can bring up to 29.1× (3.5× on average) performance improvement over the state-of-the-art baselines on two studied NVIDIA GPUs.