SCHEDULE: NOV 16-22, 2013
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Mr. Scan: Extreme Scale Density-Based Clustering Using a Tree-Based Network of GPGPU Nodes
SESSION: Engineering Scalable Applications
EVENT TYPE: Papers
TIME: 4:30PM - 5:00PM
SESSION CHAIR: Andrew M. Canning
AUTHOR(S):Benjamin R. Welton, Evan H. Samanas, Barton P. Miller
Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.
Andrew M. Canning (Chair) - Lawrence Berkeley National Laboratory
Benjamin R. Welton - University of Wisconsin - Madison
Evan H. Samanas - University of Wisconsin - Madison
Barton P. Miller - University of Wisconsin - Madison
The full paper can be found in the ACM Digital Library