GraphMat: High performance graph analytics made productive
Summary: GraphMat maps vertex programs to sparse-matrix ops on CPU, preserving productivity with native performance. Single-node C++ framework outperforms GraphLab/CombBLAS/Galois 1.1-7x, scales 13-15x on 24 cores, matching MapGraph on CPU via sparse primitives. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Narayanan Sundaram (Intel)
- 2. Nadathur Satish (Intel)
- 3. Md Mostofa Ali Patwary (Intel)
- 4. Subramanya R Dulloor (Intel)
- 5. Michael J. Anderson (Intel)
- 6. Satya Gautam Vadlamudi (Intel)
- 7. Dipankar Das (Intel)
- 8. Pradeep Dubey (Intel)
BibTeX Citation
@article{sundaram_vldb15,
title = {{GraphMat: High performance graph analytics made productive}},
author = {Sundaram, Narayanan and Satish, Nadathur and Patwary, Md Mostofa Ali and Dulloor, Subramanya R and Anderson, Michael J. and Vadlamudi, Satya Gautam and Das, Dipankar and Dubey, Pradeep},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {11},
pages = {1214--1225},
doi = {10.14778/2809974.2809988},
url = {https://doi.org/10.14778/2809974.2809988},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,539 | Distributed SociaLite: A Datalog-Based Language for Large-Scale Graph Analysis | 2013 | VLDB | 0.000104329 |
| 1,654 | Navigating the Maze of Graph Analytics Frameworks using Massive Graph Datasets | 2014 | SIGMOD | 0.00010104703 |
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