As the emerging trend of graph-based deep learning, Graph Neural Networks
(GNNs) excel for their capability to generate high-quality node feature vectors
(embeddings). However, the existing one-size-fits-all GNN implementations are
insufficient to catch up with the evolving GNN architectures, the
ever-increasing graph sizes, and the diverse node embedding dimensionalities.
To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime
system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor
explores and identifies several performance-relevant features from both the GNN
model and the input graph, and uses them as a new driving force for GNN
acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D
workload management, tailored for GNN computation to improve GPU utilization
and performance under different application settings. Third, GNNAdvisor
capitalizes on the GPU memory hierarchy for acceleration by gracefully
coordinating the execution of GNNs according to the characteristics of the GPU
memory structure and GNN workloads. Furthermore, to enable automatic runtime
optimization, GNNAdvisor incorporates a lightweight analytical model for an
effective design parameter search. Extensive experiments show that GNNAdvisor
outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph
Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$
faster), on mainstream GNN architectures across various datasets.

GNNAdvisor 是一种自适应、高效的运行时系统，旨在加速 GPU 平台上各种 GNN 工作负载，使用了新的驱动力、二维工作负载管理和 GPU 内存层次优化等技术，实现了自动运行时优化。