MetaNMP: Leveraging Cartesian-Like Product to Accelerate HGNNs with Near-Memory Processing

Heterogeneous graph neural networks (HGNNs) based on metapath exhibit powerful capturing of rich structural and semantic information in the heterogeneous graph. HGNNs are highly memory-bound and thus can be accelerated by near-memory processing. However, they also suffer from significant memory footprint (due to storing metapath instances as intermediate data) and severe redundant computation (when vertex features are aggregated among metapath instances). To address these issues, this paper proposes MetaNMP, the first DIMM-based near-memory processing HGNNs accelerator with reduced memory footprint and high performance. Specifically, we first propose a cartesian-like product paradigm to generate all metapath instances on the fly for heterogeneous graphs. In this way, metapath instances no longer need to be stored as intermediate data, avoiding significant memory consumption. We then design a data flow for aggregating vertex features on metapath instances, which aggregates vertex features along the direction of the metapath instances dispersed from the starting vertex to exploit shareable aggregation computations, eliminating most of the redundant computations. Finally, we integrate specialized hardware units in DIMM to accelerate HGNNs with near-memory processing, and introduce a broadcast mechanism for edge data and vertex features to mitigate the inter-DIMM communication. Our evaluation shows that MetaNMP achieves the memory space reduction of 51.9% on average and the performance improvement by 415.18x compared to NVIDIA Tesla V100 GPU.