CISE Postdoctoral Program: Postdoctoral Research Associate in Computational Science & Engineering Science: High Performance Computing for Remote Sensing Applications

9625668戴维斯，拉里马里兰州大学帕克分校 CISE博士后项目：计算科学工程科学博士后研究助理：高性能计算在遥感中的应用 该奖项支持CES助理大卫贝德。 拟议的培训和研究计划的主要目标是开发创新的计算方法和软件，将高性能计算应用于遥感图像数据分析中出现的基本问题。 这项工作将在马里兰州大学高级计算机研究所（UMIACS）与Davis教授（计算机科学）、Goward（地理）、Ja'Ja'（电气工程）和Townshend（地理）密切合作进行，他们正在进行一些密切相关的工作。 开发的应用程序是一个高性能的系统，用于估计净初级生产力（NPP）使用的生产效率模型（PEM）在马里兰州大学开发。 总体目标是建立一个软件模块的家庭，利用各种算法进行图像的几何校正，以消除来自外来源的信号（如大气校正），采样空间和时间，并产生衍生的地球物理特性。 这些模块将使用经过验证的便携式和实用的高性能编程语言和库构建。 我们的应用程序开发将在UMIACS的两个高性能计算平台上进行;一个16节点的IBM SP-2和一个由40个Alpha处理器组成的数字Alpha集群，通过数字ATM Gigaswitch联网。 马里兰州的设施拥有大约300 GB的在线磁盘存储和一个多TB的三级存储系统。 ***

9625668 Davis, Larry University of Maryland, College Park CISE Postdoctoral Program: Postdoctoral Research Associate in Computational Science & Engineering Science: High Performance Computing for Remote Sensing Applications This award supports CES associate David Bader. The main objective of the proposed training and research plan is to develop innovative computational methods and software for applying high performance computing to fundamental problems arising in the analysis of remotely sensed image data. This work will be conducted at the University of Maryland Institute for Advanced Computer Studies (UMIACS) in close cooperation with Professor Davis (Computer Science), Goward (Geography), Ja'Ja' (Electrical Engineering), and Townshend (Geography), who are pursuing a number of closely related efforts. The application to be developed is a high performance system for estimating net primary production (NPP) using the production efficiency model (PEM) developed at the University of Maryland. The overall aim is to build a family of software modules that make use of a variety of algorithms to carry out geometric correction of images, to eliminate signals from extraneous sources (e.g. atmospheric correction), to sample spatially and temporally, and to generate derived geophysical properties. The modules will be constructed using proven portable and practical high performance programming languages and libraries. Our applications development will be conducted on two high performance computing platforms at UMIACS; a 16-node IBM SP-2 and a Digital Alpha cluster consisting of 40 Alpha processors networked via a Digital ATM Gigaswitch. The Maryland facility has approximately 300GB of online disk storage and a multi-terabyte tertiary storage system. ***