Exploring Parallelization of Nearest Neighbor Search and Clustering in High-Dimensional Space on Emerging Parallel Architectures with Applications in Computer Vision

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This project will explore alternative parallelization techniques for very largescale data intensiveapplications on emerging new parallel architectures, including distributed memory computerclusters using multicore and GPGPU processors. The experimental environment includes anapplication and system for large scale image classification and orientation suitable for use ingeographic information systems. A variety of evaluation hardware is expected to be availablefor the project. The ultimate goal of this research is to leverage large scale systems, especiallystorage, to provide services to end users in the form of geospatial data. The proposed researchwill leverage preliminary work by the PIs in the application of CUDA and MPI programming andwill demonstrate the application of these technologies to very large scale storage, potentiallyholding multiple terabytes of aerial and satellite imagery.