diff --git a/res/papers/06-scaling-graph.json b/res/papers/06-scaling-graph.json index c37eb80..66ed321 100644 --- a/res/papers/06-scaling-graph.json +++ b/res/papers/06-scaling-graph.json @@ -1,6 +1,19 @@ { "title": "Training Systems for Scaling Graphs", "paper": [ + { + "venue": "DaMoN 2024", + "name": "In situ neighborhood sampling for large-scale GNN training", + "affiliation":"Boston University", + "link": "https://dl.acm.org/doi/10.1145/3662010.3663443", + "source":"https://github.com/CASP-Systems-BU/damon24-gnn-in-situ-sampling/" + }, + { + "venue": "HPCA 2024", + "name": "BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing", + "affiliation": "UCLA", + "link": "https://ieeexplore.ieee.org/abstract/document/10476427" + }, { "venue": "EuroSys 2023", "name": "MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks", @@ -14,6 +27,19 @@ "affiliation": "ByteDance", "link": "https://dl.acm.org/doi/abs/10.14778/3514061.3514069" }, + { + "venue": "VLDB 2022", + "name": "Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching", + "affiliation": "Seoul National University", + "link": "https://dl.acm.org/doi/10.14778/3551793.3551819", + "source": "i.wendabao-a.net/?inviter=18436&utm_source=invite#/?inviter=18436" + }, + { + "venue": "ISCA 2022", + "name":"SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures", + "affiliation": "KAIST", + "link":"https://dl.acm.org/doi/10.1145/3470496.3527391" + }, { "venue": "ICML 2022", "name": "GraphFM: Improving Large-Scale GNN Training via Feature Momentum",