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NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters
Summary: NeutronHeter: top-down multi-level mapping on a tree-like resource graph (hierarchical clustering of compute and bandwidth) to solve the multi-constrained multi-way GNN placement problem in heterogeneous clusters. Plus adaptive communication migration via selective vertex replication to bypass low-bandwidth links, reducing asymmetric communication hotspots and yielding 1.06–33.05x speedups over SOTA.
(summarized by gpt-5-mini on Feb 11 2026)
- Paper ID
- 7332
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,027 | 30.25%
- DOI
-
10.1145/3749175
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 278 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029230623 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 1,942 |
Heterogeneity-aware Distributed Parameter Servers |
2017 |
SIGMOD |
0.00010012691 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 2,422 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8499665e-05 |
| 3,025 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6906935e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 5,136 |
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments |
2024 |
VLDB |
5.6723526e-05 |
| 6,884 |
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines |
2023 |
VLDB |
4.8955332e-05 |
| 6,942 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8922884e-05 |
| 6,980 |
OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine |
2024 |
VLDB |
4.8744298e-05 |
| 7,004 |
RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing |
2024 |
VLDB |
4.8656632e-05 |
| 7,091 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
4.8370645e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 7,545 |
XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store |
2024 |
VLDB |
4.714889e-05 |
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| 10,298 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1945683e-05 |