SCHEDULE: NOV 12-18, 2011

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Parallel Algorithms for Clustering and Nearest Neighbor Search in High Dimensions

SESSION: Research Poster Reception

EVENT TYPE: ACM Student Research Competition Poster, Poster, Electronic Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR: Bernd Mohr

AUTHOR(S):Logan Moon, Daniel Long, Shreyas Joshi, Vyomkesh Tripathi, Bo Xiao, George Biros

ROOM:WSCC North Galleria 2nd/3rd Floors

ABSTRACT:
Although there is a significant body of work for efficient clustering and nearest neighbor searches in database systems, surprisingly little work has been done on algorithms and libraries for high-end scientific computing platforms We present a set of tools implemented using MPI and OpenMP for massively parallel computational geometry problems. We combine parallel distance and filtering operations and kmeans clustering algorithms with an optimized seeding procedure, locality-sensitive hashing, and a novel parallel indexing structure, “SPRINKLE-TREE”, to support exact and approximate operations for problems in high dimension. The overall scheme shows excellent scalability and enables the analysis of datasets of unprecedented scale. In our largest runs, we were able to conduct exact searches and clustering on a 45TB dataset in under 30 seconds on 98K cores on the Kraken platform. Tree construction takes under three seconds for a dataset with over 20 million points in 100 dimensions on 12288 cores.

Chair/Author Details:

Bernd Mohr (Chair) - Juelich Supercomputing Centre

Logan Moon - University of Texas at Austin

Daniel Long - Georgia Institute of Technology

Shreyas Joshi - Georgia Institute of Technology

Vyomkesh Tripathi - Georgia Institute of Technology

Bo Xiao - Georgia Institute of Technology

George Biros - University of Texas at Austin

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