Computer Sciences Commons^™

An Efficient Parallel Algorithm For High Dimensional Similarity Join, Khaled Alsabti, Sanjay Ranka, Vineet Singh

Electrical Engineering and Computer Science - All Scholarship

Multidimensional similarity join finds pairs of multi-dimensional points that are within some small distance of each other: The 6-k-d-B tree has been proposed as a data structure that scales better as the number of dimensions in-creases compared to previous data structures. We present a cost model of the E-k-d-B tree and use it to optimize the leaf size. We present novel parallel algorithms for the similarity join using the E-k-d-B tree. A load-balancing strategy based on equi-depth histograms is shown to work well for uniform or low-skew situations, whereas another based on weighted equi-depth histograms works far better for high-skew …

Parallel Probabilistic Computations On A Cluster Of Workstations, Atanas Radenski, Andrew Vann, Boyana Norris

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

Probabilistic algorithms are computationally intensive approximate methods for solving intractable problems. Probabilistic algorithms are excellent candidates for cluster computations because they require little communication and synchronization. It is possible to specify a common parallel control structure as a generic algorithm for probabilistic cluster computations. Such a generic parallel algorithm can be glued together with domain-specific sequential algorithms in order to derive approximate parallel solutions for different intractable problems.

In this paper we propose a generic algorithm for probabilistic computations on a cluster of workstations. We use this generic algorithm to derive specific parallel algorithms for two discrete optimization problems: the …

A Fast Parallel Algorithm To Recognize P4-Sparse Graphs, Rong Lin, Stephan Olariu

Computer Science Faculty Publications

A number of problems in mobile computing, group-based collaboration, automated theorem proving, networking, scheduling, and cluster analysis suggested the study of graphs featuring certain “local density” characteristics. Typically, the notion of local density is equated with the absence of chordless paths of length three or more. Recently, a new metric for local density has been proposed, allowing a number of such induced paths to occur. More precisely, a graphG is called P4-sparse if no set of five vertices inG induces more than one chordless path of length three. P4-sparse graphs generalize the well-known class of cographs corresponding to …