Poster: parallel algorithms for clustering and nearest neighbor search problems in high dimensions

Clustering and nearest neighbor searches in high dimensions are fundamental components of computational geometry, computational statistics, and pattern recognition. Despite the widespread need to analyze massive datasets, no MPI-based implementations are available to allow this analysis to be scaled to modern highly parallel platforms. We seek to develop a set of algorithms that will provide unprecedented scalability and performance for these fundamental problems.