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Self-adaptive Graph Traversal on GPUs

Summary: Self-adaptive graph traversal on GPUs; no preprocessing; operates on universal graphs. Proposes Tiled Partition and Resident Tile Stealing for runtime GPU use, plus Sampling-based Reordering for memory efficiency; beats preprocessing-based methods across GPU deployments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6170
Venue
SIGMOD
Year
2021
Pagerank
4.7956162e-05
Overall Rank
7,225 | 49.74%
DOI
10.1145/3448016.3457279

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
10,079 Fast Optimal Group Steiner Tree Search using GPUs 2026 SIGMOD 4.1945683e-05
10,308 Efficient Partition-based Approaches for Diversified Top-k Subgraph Matching 2026 VLDB 4.1945683e-05
10,311 Efficient Temporal Edge-Core Maintenance in Streaming Graphs 2026 VLDB 4.1945683e-05
10,948 gSWORD: GPU-accelerated Sampling for Subgraph Counting 2024 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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