Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining
Summary: Introduces a non-parametric, self-tunable sparse-matrix representation and tiling scheme tailored to power-law graphs for GPU SpMV. Demonstrates improvements over prior GPU methods on PageRank, HITS, and Random Walk with Restart using real web graphs. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xintian Yang (Ohio State University)
- 2. Srinivasan Parthasarathy (Ohio State University)
- 3. P. Sadayappan (Ohio State University)
BibTeX Citation
@article{yang_vldb11,
title = {{Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining}},
author = {Yang, Xintian and Parthasarathy, Srinivasan and Sadayappan, P.},
journal = {PVLDB},
series = {{VLDB} '11},
volume = {4},
number = {4},
pages = {231--242},
doi = {10.14778/1938545.1938548},
url = {https://doi.org/10.14778/1938545.1938548},
year = {2011}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,686 | Accelerating Triangle Counting on GPU | 2021 | SIGMOD | 7.2029305e-05 |
| 4,157 | GPU-based Graph Traversal on Compressed Graphs | 2019 | SIGMOD | 6.8629037e-05 |
| 4,171 | Accelerating Dynamic Graph Analytics on GPUs | 2018 | VLDB | 6.8537202e-05 |
| 4,225 | Realtime Top-k Personalized PageRank over Large Graphs on GPUs | 2020 | VLDB | 6.821373e-05 |
| 8,179 | GraphINC: Graph Pattern Mining at Network Speed | 2023 | SIGMOD | 5.472762e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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