High Performance Computing for Land Cover Dynamics

9318183戴维斯大挑战应用小组竞赛为支持多学科科学家和工程师团队提供了一种机制，以实现1993财政年度高性能计算和通信（HPCC）计划的目标。理想的提案不仅提供了在以下方面取得重大进展的机会：(1)科学或工程中的基本问题，其解决方案可以通过应用高性能计算技术和资源来推进，或(2)促进这些进展的使能技术，而且还提供了科学和计算活动之间的重要互动，通常涉及数学、计算机或计算科学家；这将对正在研究的特定科学或工程问题领域或学科之外的高性能计算活动产生影响。研究人员将研究将高性能并行计算应用于遥感中一类具有重要科学意义和计算要求的问题——土地覆盖动力学问题，包括为全球碳循环、水文建模和全球生态对气候变化和人类活动的响应生成改进的精细空间分辨率数据。这项研究是由马里兰大学、印第安纳大学、新罕布什尔大学和美国宇航局戈达德航天中心的科学家合作进行的。该合同将结合对遥感数据新分析程序的研究，将多光谱、多分辨率和多时间图像数据集集成到基于分层数据结构（即四叉树）的统一全球数据结构中。用于表示空间数据（从图像分析开发的地图和产品）的并行数据结构和具有复杂查询语言的空间数据库系统的开发，科学家可以使用该查询语言来控制生物物理模型对全局数据集的应用-为构建涉及不规则数据结构操作的可扩展和并行解决方案的运行时支持例如四叉树并行l/O，特别是将大型阵列和四叉树映射到并行磁盘和磁盘系统上的新方法，以及使用低开销的批量传输访问它们。开发工作将在安装在马里兰大学的32处理器连接机CM5和IBM SP1上进行，我们建议将其作为程序的一部分获得。该奖项由美国高级项目研究局以及美国国家科学基金会在地质、生物和计算机科学方面的项目提供支持。

9318183 Davis The Grand Challenge Application Groups competition provides one mechanism for the support of multidisciplinary teams of scientists and engineers to meet the goals of the High Performance Computing and Communications (HPCC) Initiative in Fiscal Year 1993. The ideal proposal provided not only the opportunity to achieve significant progress on (1) a fundamental problem in science or engineering whose solution could be advanced by applying high performance computing techniques and resources, or (2) enabling technologies which facilitate those advances but also significant interactions between scientific and computational activities, usually involving mathematical, computer or computational scientist, that would have impact in high performance computational activities beyond the specific scientific or engineering problem area(s) or discipline being studied. The investigators will study the application of high performance parallel computing to a class of scientifically important and computationally demanding problems in remote sensing- land cover dynamics problems including generating improved- fine spatial resolution data for the global carbon cycle, hydrological modeling and global ecological responses to climate changes and human activity. The research is collaborative, including scientist from the University of Maryland, University of Indiana, University of news Hampshire and NASA's Goddard Space Center. The award will combine research on -new analysis procedures for remotely sensed data -the integration of multispectral, multiresolution and multitemporal image data sets into a unified global data structure based on hierarchical data structures (i.e., quadtrees) -the utilization of these hierarchical, parallel data structures for the representation of spatial data (maps and products developed from image analysis) and the development of a spatial data base system with a sophisticate d query language that scientist can use to control the application of biophysical models to global data sets -run-time support for constructing scalable and parallel solutions to problems involving the manipulation of irregular data structures such as quadtrees -parallel l/O,especially novel methods for mapping large arrays and quadtrees onto parallel disks and disk systems, and for accessing them using low overhead bulk transfers The development work will be conducted on a 32 processor Connection Machine CM5, installed at the University of Maryland, and on an IBM SP1 which we propose to obtain as part of the program. This award is being supported by the Advanced Projects Research Agency as well as NSF programs in geological, biological, and computer sciences.