ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909030153191424 |
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| author | Naman, Pranjal Simmhan, Yogesh |
| author_facet | Naman, Pranjal Simmhan, Yogesh |
| contents | Graph Neural Network (GNN) inference on billion-scale graphs is critical for domains like fintech and recommendation systems. Full-graph inference on these large graphs can be challenging due to high communication costs in distributed settings and high I/O costs in disk-backed Out-of-Core (OOC) settings. Existing OOC systems, operating across disk and memory, primarily focus on GNN training and perform poorly for full-graph inference due to massive read amplification, irregular I/O, and memory pressure. We present ATLAS, a disk-based GNN inference framework that enables efficient full-graph, layer-wise inference on graphs whose topologies, features and intermediate embeddings exceed the available memory on single machines. ATLAS replaces gather-based execution with a broadcast-based model that enables sequential, single-pass streaming reads of features and embeddings per layer. A tiered memory-disk hierarchy with minimum-pending-message eviction, graph reordering and a GPU-accelerated pipeline sustains high throughput within $128$ GiB RAM and $2$ TiB SSD. Across out-of-core graphs with up to $4$B edges and $550$ GiB features and multiple GNN architectures, ATLAS improves end-to-end inference time by $\approx12$--$30\times$ over State-of-the-Art (SOTA) OOC baselines on a single workstation, while remaining within $\approx5\%$ when features fit in memory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks Naman, Pranjal Simmhan, Yogesh Distributed, Parallel, and Cluster Computing Graph Neural Network (GNN) inference on billion-scale graphs is critical for domains like fintech and recommendation systems. Full-graph inference on these large graphs can be challenging due to high communication costs in distributed settings and high I/O costs in disk-backed Out-of-Core (OOC) settings. Existing OOC systems, operating across disk and memory, primarily focus on GNN training and perform poorly for full-graph inference due to massive read amplification, irregular I/O, and memory pressure. We present ATLAS, a disk-based GNN inference framework that enables efficient full-graph, layer-wise inference on graphs whose topologies, features and intermediate embeddings exceed the available memory on single machines. ATLAS replaces gather-based execution with a broadcast-based model that enables sequential, single-pass streaming reads of features and embeddings per layer. A tiered memory-disk hierarchy with minimum-pending-message eviction, graph reordering and a GPU-accelerated pipeline sustains high throughput within $128$ GiB RAM and $2$ TiB SSD. Across out-of-core graphs with up to $4$B edges and $550$ GiB features and multiple GNN architectures, ATLAS improves end-to-end inference time by $\approx12$--$30\times$ over State-of-the-Art (SOTA) OOC baselines on a single workstation, while remaining within $\approx5\%$ when features fit in memory. |
| title | ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2605.09402 |