GNN programming abstraction paper is accepted by IEEE Computer Architecture Letters!

👏 Paper title: Architectural Implications of GNN Aggregation Programming Abstractions. This paper evaluates the architectural implications of programming abstractions for Graph Neural Network (GNN) aggregation. It introduces a taxonomy based on data organization and propagation methods and performs a comprehensive performance characterization across platforms and graph properties. Key findings include insights into abstraction selection, hardware adaptability, and the structural impact of graphs, providing valuable guidance for GNN acceleration research. [related project]