EmptyHeaded: A Relational Engine for Graph Processing
Summary: EmptyHeaded is a high-level, datalog-like relational graph engine with a novel SIMD-enabled join architecture and data layouts. New optimizer and layouts deliver up to 1,000× speedups vs other high-level systems on graph patterns, PageRank, SSSP, and match Galois on PageRank. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Christopher R. Aberger (Stanford University)
- 2. Susan Tu (Stanford University)
- 3. Christopher Ré (Stanford University)
- 4. Kunle Olukotun (Stanford University)
BibTeX Citation
@inproceedings{aberger_sigmod16,
title = {{EmptyHeaded: A Relational Engine for Graph Processing}},
author = {Aberger, Christopher R. and Tu, Susan and Ré, Christopher and Olukotun, Kunle},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2915213},
url = {https://dl.acm.org/doi/10.1145/2882903.2915213},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
Showing 42 of 92 citing papers.
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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