The principle studies of this lab are parallel and distributed computing with multi-core processors and clusters composed of multi-core processors. The current focus of our studies are in two domains.  The first is developing high-performance methods for analyzing high-dimensional "big data" sets.  These studies exploit random and approximate methods for manipulating and analyzing the high-dimensional data.  Most recently we are beginning to explore strategies to accelerate Topological Data Analysis (TDA) to enable the effective application of TDA methods to high-dimensional  big data sets.  The second application for our HPC studies lies with improving optimistic synchronization methods for high-performance parallel discrete event driven simulation.