Compiler and Runtime Support for Data Intensive Computing on Multi-dimensional Data

多维数据数据密集型计算的编译器和运行时支持

• 批准号: 9982087
• 负责人: Alan Sussman
• 金额: $ 42.63万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-03-01 至 2004-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9982087&HistoricalAwards=false
• 关键词: Compiler; Runtime; Support; Data; Intensive

One of the largest and fastest-growing problems in scientific computing is the analysis and processing of very large data sets. These scientific data sets can come from long-running simulations (e.g. simulations of water pollution that create "snapshots" of the expected water conditions at later times), archives of remote sensing data (e.g. high-resolution satellite imagery), and archives of medical images (e.g. MRI scans for a patient or group of patients). These data sets are usually multi-dimensional, including spatial coordinates, time stamps, and several physical properties at each point. Several systems now support storage, retrieval, and visualization of such data sets, but few can efficiently process the data. This project will develop methods to produce efficient programs to carry out multi-dimensional data processing and analysis using a high-level parallel language.The project will attack this problem by developing runtime routines for optimizing resource usage, appropriate language extensions, and aggressive compiler optimizations for large data processing. The runtime methods will implement policies that optimize computational efficiency on a broad range of large data set analyses, taking into account the spatial structure and partitioning of the data and the computation to be performed. Incorporating these routines into the investigator's Active Data Repository will substantially generalize and improve that system. The language extensions and compiler optimizations will then make use of the runtime system to enable applications that analyze multi-dimensional data sets to be expressed at an abstract level, yet achieve high utilization of computational, storage, and communication resources.

科学计算中最大和增长最快的问题之一是分析和处理非常大的数据集。这些科学数据集可以来自长期运行的模拟（例如，水污染模拟，创建稍后预期水状况的“快照”），遥感数据档案（例如，高分辨率卫星图像）和医学图像档案（例如，对一名患者或一组患者的MRI扫描）。这些数据集通常是多维的，包括空间坐标，时间戳和每个点的几个物理属性。现在有几个系统支持这些数据集的存储、检索和可视化，但很少有系统能有效地处理这些数据。本项目将开发使用高级并行语言进行多维数据处理和分析的高效程序的方法。本项目将通过开发用于优化资源使用的运行时例程、适当的语言扩展以及针对大型数据处理的积极编译器优化来解决这一问题。运行时方法将实现优化大范围大型数据集分析的计算效率的策略，同时考虑数据的空间结构和分区以及要执行的计算。将这些例行程序纳入调查员的活动数据储存库将大大推广和改进该系统。然后，语言扩展和编译器优化将利用运行时系统，使分析多维数据集的应用程序能够在抽象级别上表达，同时实现计算，存储和通信资源的高利用率。