Benchmarking GNN-Based Recommender Systems on Intel Optane Persistent Memory

Graph neural networks (GNNs), which have emerged as an effective method for handling machine learning tasks on graphs, bring a new approach to building recommender systems, where the task of recommendation can be formulated as the link prediction problem on user-item bipartite graphs. Training GNN-based recommender systems (GNNRecSys) on large graphs incurs a large memory footprint, easily exceeding the DRAM capacity on a typical server. Existing solutions resort to distributed subgraph training, which is inefficient due to the high cost of dynamically constructing subgraphs and significant redundancy across subgraphs. The emerging Intel Optane persistent memory allows a single machine to have up to 6 TB of memory at an affordable cost, thus making single-machine GNNRecSys training feasible, which eliminates the inefficiencies in distributed training. One major concern of using Optane for GNNRecSys is Optane's relatively low bandwidth compared with DRAMs. This limitation can be particularly detrimental to achieving high performance for GNNRecSys workloads since their dominant compute kernels are sparse and memory access intensive. To understand whether Optane is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive benchmarking study. Our benchmarking results show that when properly configured, Optane-based single-machine GNNRecSys training outperforms distributed training by a large margin, especially when handling deep GNN models. We analyze where the speedup comes from, provide guidance on how to configure Optane for GNNRecSys workloads, and discuss opportunities for further optimizations.