SSDs are traditionally regarded as a cheap but slow way to scale up GNN training. Several GNN systems explore cheap single-machine single-GPU out-of-core training but fall short in terms of TPC (throughput per monetary cost). The underlying reason is that the existing systems 1) overly focus on minimizing the number of SSD accesses, which results in substantial unnecessary overhead on the CPU side, or 2) exhaust all GPU parallelism to saturate SSD but fail to overlap SSD accesses with GNN computation. In this work, we present Hyperion, a cost-efficient system for terabyte-scale GNN training. We argue that co-optimizing GPU-initiated asynchronous SSD access and GNN computation pipeline enables us to only add cheap NVMe SSDs, rather than expensive GPU servers, to achieve in- memory-like throughput and thus maximal TPC of GNN training. However, this is non-trivial due to imbalanced workloads and interference among IO submission, IO completion, and cache lookup. To tackle the challenges, Hyperion proposes three key designs. First, Hyperion proposes the first GPU-initiated pipeline- friendly asynchronous disk IO stack, which only requires about 1% GPU cores to saturate SSD throughput and wastes no GPU cores between IO submission and completion to fully overlap disk IO and computation. Second, we propose a new GPU-managed, disaggregated, and unified cache that disaggregates cache lookup from disk IO and fully utilizes CPU/GPU memory hierarchy by a unified static cache policy. Third, we propose a GNN-aware general TPC-analytical model that precisely predicts TPC under diverse hardware settings and GNN models and provide a hint to guide users to select hardware, e.g., number of SSDs, under a limited budget to maximize TPC. Experiments demonstrate that Hyperion can improve the TPC by over 3.1x on terabyte-scale graphs compared to SOTA out-of-core baselines and improve 60 x TPC compared to distributed in-memory baselines.

Online GNN inference has been widely explored by applications such as online recommendation and financial fraud detection systems, where even minor delays can result in significant financial impact. Real-time dynamic graph sampling enables online GNN inference to reflect the latest graph updates in real-world graphs. However, online GNN inference typically demands millisecond-level latency Service Level Objectives (SLOs) as its performance guarantees, which poses great challenges for existing dynamic graph sampling approaches based on graph databases. The issues mainly arise from two aspects: long tail latency due to imbalanced data-dependent sampling and large communication overhead incurred by distributed sampling. To address these issues, we propose Helios, an efficient distributed dynamic graph sampling service to meet the stringent latency SLOs. The key ideas of Helios are 1) pre-sampling the dynamic graph in an event-driven approach, and 2) maintaining a query-aware sample cache to build the complete K-hop sampling results locally for inference requests. Experiments on multiple datasets show that Helios achieves up to 67× higher serving throughput and up to 32× lower P99 query latency compared to baselines.

Graph Neural Networks (GNNs) are widely employed in applications like recommendation systems, social network analysis, and fraud detection, but training large-scale GNNs is challenging due to its memory limitations. Existing systems face a trade-off between throughput and monetary cost: Distributed systems require expensive memory scaling, while single-machine out-of-core systems are limited by GPU/PCIe throughput. To this end, we propose Moment, a physical communication topology and data placement co-optimizer to enable high-throughput and low-cost GNN training in a single multi-GPU machine. Moment addresses communication contention and GPU load imbalance issues by modeling the physical topology as capacity-constrained directed graphs and formulating communication scheduling as a max-flow problem. It also introduces a data-distribution-aware knapsack algorithm for optimized data placement. Experimental results show that Moment outperforms out-of-core systems by up to 6.51 × and distributed systems by up to 3.02 ×, with only 50% monetary cost.