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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)

Paper ID
5293
Venue
SIGMOD
Year
2016
Pagerank
7.1593902e-05
Overall Rank
3,740 | 74.35%
DOI
10.1145/2882903.2915204

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kim_sigmod16,
        title = {{GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs}},
        author = {Kim, Min-Soo and An, Kyuhyeon and Park, Himchan and Seo, Hyunseok and Kim, Jinwook},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2915204},
        url = {https://dl.acm.org/doi/10.1145/2882903.2915204},
        year = {2016}
}

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