SOFTWARE: High-level Programming Methodologies for Data Intensive Computations

对于许多科学和工程领域的进步来说，解决具有大量计算和存储需求的问题正变得越来越关键。运行时和编译器技术可以在非常高的抽象级别上实现针对这些问题的算法编程，从而显著加快这些领域的进步。我们的研究集中在一类重要的科学和工程问题上：数据密集型计算。这种计算出现在科学和工程研究的许多领域。我们的目标是针对这类计算的两个高级编程模型：1.面向对象(基于Java)：面向对象的特性，如封装和多态，可以简化软件开发，并允许更好地长期管理大型计算项目。我们使用一种Java方言来表示数据密集型计算。已经为这种Java方言构建了一个初步的原型编译器。我们的编译器使用静态过程间切片技术来提取所需的函数，以利用现有的运行时系统-活动数据存储库(ADR)。2.声明性的(基于XQL的)：声明性语言的非常高级别的性质可以允许对新的计算技术进行快速原型设计。XML(可扩展标记语言)正被接受为数据逻辑布局的格式，这些数据可以通过Web获得或在组织之间交换。我们将对XML模式进行扩展，以表示元素的多维集合。我们还将对XQL进行扩展，XQL是一种用于XML文档的查询语言，用于表示对大型数据集的处理。我们在五个重要方面进行了创新性的研究：1.磁盘驻留数据集的执行策略。2.高级符号分析。3.面向对象程序的过程间代码移动技术。4.编译器和运行时相结合的局部性管理技术。5.面向对象语言的高级过程间切片技术。

Solving problems that have large computational and storage requirements is becoming increasingly critical for advances in many domains of science and engineering. Runtime and compiler technology can significantly accelerate advances in such domains by enabling the programming of algorithms for these problems at very high levels of abstraction. Our research focuses on an important class of scientific and engineering problems: data intensive computations. Such computations arise in many domains of scientific and engineering research. We are targetting two high-level programming models for this class of computations:1. Object-Oriented (Java-Based): Object-oriented features like encapsulation and polymorphism can ease software development and allow better long-term management of large computational projects. We are using a dialect of Java for expressing data intensive computations. An initial prototype compiler for this dialect of Java has been built. Our compiler uses static interprocedural slicing technique to extracting required functions for utilizing an existing runtime system, Active Data Repository (ADR). 2. Declarative (XQL-Based): The very high-level nature of declarative languages can allow rapid prototyping of new computational techniques. XML (Extensible Markup Language) is getting accepted as the format for the logical layout of data which may be made available over the web or exchanged between organizations. We will make extensions to XML Schemas for expressing multidimensional collections of elements. We also will also make extensions to XQL, which is a query language for XML documents, for expressing processing of large datasets. We propose innovative research in five important areas:1. Execution Strategies for Disk Resident Datasets. 2. Advanced Symbolic Analysis. 3. Interprocedural Code Motion Techniques for Object-Oriented Programs. 4. Combined Compiler and Runtime Techniques for Locality Management. 5. Advanced Interprocedural Slicing Techniques for Object-Oriented Languages.