GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs
Summary: GTS enables fast GPU graph processing on a single machine by streaming topology from SSDs to thousands of GPU cores, avoiding inter-machine partitioning. Designed for RMAT32-scale graphs (~64B edges), it outperforms GraphX, Giraph, PowerGraph, TOTEM in experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Min-Soo Kim
- 2. Kyuhyeon An
- 3. Himchan Park
- 4. Hyunseok Seo
- 5. Jinwook Kim
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,276 | Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching | 2022 | VLDB | 7.2879718e-05 |
| 3,670 | A Distributed Multi-GPU System for Fast Graph Processing | 2018 | VLDB | 6.8567044e-05 |
| 4,522 | GPU-based Graph Traversal on Compressed Graphs | 2019 | SIGMOD | 6.1146374e-05 |
| 5,799 | CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor | 2024 | VLDB | 5.3219334e-05 |
| 6,668 | TrillionG: A Trillion-scale Synthetic Graph Generator using a Recursive Vector Model | 2017 | SIGMOD | 4.968252e-05 |
| 6,745 | DistME: A Fast and Elastic Distributed Matrix Computation Engine using GPUs | 2019 | SIGMOD | 4.9417155e-05 |
| 10,044 | ACGraph: An Efficient Asynchronous Out-of-Core Graph Processing Framework | 2026 | SIGMOD | 4.1945683e-05 |
| 10,514 | cuMatch: A GPU-based Memory-Efficient Worst-case Optimal Join Processing Method for Subgraph Queries with Complex Patterns | 2025 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019005923 |
| 37 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.0007522744 |
| 70 | Hive - A Warehousing Solution Over a Map-Reduce Framework | 2009 | VLDB | 0.00059533166 |
| 2,754 | Giraph Unchained: Barrierless Asynchronous Parallel Execution in Pregel-like Graph Processing Systems | 2015 | VLDB | 8.169411e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,670 | A Distributed Multi-GPU System for Fast Graph Processing | 2018 | VLDB | 6.8567044e-05 |
| 1,685 | Fast Iterative Graph Computation with Block Updates | 2013 | VLDB | 0.0001091808 |
| 10,863 | Towards Sufficient GPU-accelerated Dynamic Graph Management: Survey and Experiment | 2025 | VLDB | 4.1945683e-05 |
| 1,877 | Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation | 2015 | VLDB | 0.00010236803 |
| 4,577 | Accelerating Dynamic Graph Analytics on GPUs | 2018 | VLDB | 6.0709631e-05 |
| 10,079 | Fast Optimal Group Steiner Tree Search using GPUs | 2026 | SIGMOD | 4.1945683e-05 |
| 5,799 | CGgraph: An Ultra-fast Graph Processing System on Modern Commodity CPU-GPU Co-processor | 2024 | VLDB | 5.3219334e-05 |
| 5,017 | TurboGraph++: A Scalable and Fast Graph Analytics System | 2018 | SIGMOD | 5.7574792e-05 |
| 10,705 | Efficient Graph Data Access for Out-of-Memory GPU Streaming Graph Processing | 2025 | VLDB | 4.1945683e-05 |
| 4,522 | GPU-based Graph Traversal on Compressed Graphs | 2019 | SIGMOD | 6.1146374e-05 |