@inproceedings{f201f73ee68d4d26914c1ff8a8416c8c,
title = "Graph coarsening and clustering on the GPU",
abstract = "Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph coarsening algorithm and use this to implement a parallel agglomerative clustering heuristic on both the CPU and the GPU. This heuristic is able to generate clusterings in very little time: a modularity 0.996 clustering is obtained from a street network graph with 14 million vertices and 17 million edges in 4.6 seconds on the GPU.",
author = "{Fagginger Auer}, B.O. and R.H. Bisseling",
note = "10th DIMACS Implementation Challenge Workshop, February 13-14, 2012",
year = "2013",
doi = "10.1090/conm/588/11706",
language = "English",
isbn = "978-0-8218-9038-7 ",
volume = "588",
series = "Contemporary Mathematics",
publisher = "American Mathematical Society",
pages = "223--240",
editor = "Bader, {David A.} and Henning Meyerhenke and Peter Sanders and Dorothea Wagner",
booktitle = "Graph Partitioning and Graph Clustering",
address = "United States",
}