Nezha: An Efficient Distributed Graph Processing System on Heterogeneous Hardware
Summary: Nezha is a distributed graph system on CPU+GPU nodes using RDMA to reduce communication and improve compute utilization. Key ideas: graph-friendly RDMA protocol, multi-device cooperation, and a workload balancer, with clear gains on popular benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Pengjie Cui
- 2. Haotian Liu
- 3. Dong Jiang
- 4. Bo Tang
- 5. Ye Yuan
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,044 | ACGraph: An Efficient Asynchronous Out-of-Core Graph Processing Framework | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,081 | Subgraph Matching over Graph Federation | 2022 | VLDB | 5.2208051e-05 |
| 3,670 | A Distributed Multi-GPU System for Fast Graph Processing | 2018 | VLDB | 6.8567044e-05 |
| 13,295 | NeMeSys - A Showcase of Data Oriented Near Memory Graph Processing | 2019 | SIGMOD | - |
| 3,232 | Managing Large Dynamic Graphs Efficiently | 2012 | SIGMOD | 7.336861e-05 |
| 4,234 | Distributed Edge Partitioning for Trillion-edge Graphs | 2019 | VLDB | 6.3355073e-05 |
| 5,799 | CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor | 2024 | VLDB | 5.3219334e-05 |
| 7,493 | Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks | 2023 | VLDB | 4.7180617e-05 |
| 10,638 | Heta: Distributed Training of Heterogeneous Graph Neural Networks | 2025 | VLDB | 4.1945683e-05 |
| 1,877 | Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation | 2015 | VLDB | 0.00010236803 |
| 10,027 | NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters | 2026 | SIGMOD | 4.1945683e-05 |