IEEE CAL paper on architectural implications of GNN aggregation programming abstractions

👏 Paper title: Architectural Implications of GNN Aggregation Programming Abstractions.

GNN aggregation is often expressed through high-level programming abstractions, but different abstractions can imply very different data movement, parallelism, and memory-access behavior. This paper studies the architectural consequences of these abstraction choices.

The work builds a taxonomy around data organization and propagation patterns, then characterizes performance across graph properties and hardware platforms. The resulting analysis helps clarify when an aggregation abstraction is friendly to acceleration and when it may introduce hidden inefficiencies.

This is useful because GNN software abstractions are often selected for programming convenience, but they can strongly influence memory traffic, scheduling opportunities, and hardware utilization. The paper makes these implications visible and measurable.

For accelerator and framework designers, the study provides guidance on matching aggregation APIs to hardware behavior. It also helps identify which abstraction patterns are likely to scale across graph datasets and which may require architecture-specific optimization.