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Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

Summary: Non-parametric, self-tunable data representation and a novel tiling scheme optimize SpMV on GPUs for power-law graphs. Auto-tuning with a runtime performance model enables adaptive parameters and scalable multi-GPU graph mining (PageRank, HITS, RWR). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10253
Venue
VLDB
Year
2011
Pagerank
6.3213177e-05
Overall Rank
4,254 | 70.41%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
4,168 Accelerating Triangle Counting on GPU 2021 SIGMOD 6.391271e-05
4,522 GPU-based Graph Traversal on Compressed Graphs 2019 SIGMOD 6.1146374e-05
4,577 Accelerating Dynamic Graph Analytics on GPUs 2018 VLDB 6.0709631e-05
4,671 Realtime Top-k Personalized PageRank over Large Graphs on GPUs 2020 VLDB 6.0085645e-05
8,273 GraphINC: Graph Pattern Mining at Network Speed 2023 SIGMOD 4.5450209e-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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