The PIs are working on developing and evaluating a data-driven three-phase adaptive, sparse multicore data mining framework for scalable and efficient supervised classification and statistical analysis.
Phase-I seeks to characterize data attributes in terms of sparsity, graph-theoretic structure and geometric and numeric measures toward data transformations with a focus on dimensionality reduction. The goal is to explore the trade-offs between quality of solution (accuracy and precision of classification) and total work (sequential computational costs) toward faster, yet improved methods.
Phase-II operates on the transformed data to increase the degree of fine to coarse grained concurrency while restructuring the data for enhanced reuse and locality of access. This phase provides a weighted annotated graph model of the computations indicating dependencies, data sharing measures and computational costs.
Phase-III utilizes this model to formulate and explore architecture-aware mappings of data mining computations to the multicore processors, including cache and bandwidth aware thread-to-core mappings that consider both performance and power.
The PIs thus seek adaptations to utilize data set attributes, including approximations and concurrency of computations latent in the sparsity structure, toward improved utilization of processor and memory hardware on current and future multicores with larger core counts, complex cache hierarchies and off-chip bandwidth constraints.
