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

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这个项目将探索替代并行技术非常大规模的数据密集型应用程序的新兴并行架构，包括分布式内存计算机集群使用多核和GPGPU处理器。该实验环境包括一个适用于地理信息系统的大规模图像分类和定位的应用程序和系统。各种评估硬件预计将可用于该项目。本研究的最终目标是利用大规模系统，特别是存储，以地理空间数据的形式向最终用户提供服务。拟议的研究将利用初步工作的PI在CUDA和MPI编程的应用，并将展示这些技术的应用非常大规模的存储，可能持有数TB的航空和卫星图像。

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.