SOFTWARE: High-level Programming Methodologies for Data Intensive Computations

解决具有大量计算和存储需求的问题对于许多科学和工程领域的进步变得越来越重要。编译器和编译器技术可以通过在非常高的抽象级别上为这些问题编程算法来显着加速这些领域的进步。我们的研究重点是一类重要的科学和工程问题：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。我们针对这类计算的两个高级编程模型：1。面向对象（基于Java）：封装和多态性等面向对象的特性可以简化软件开发，并允许更好地长期管理大型计算项目。我们使用Java的方言来表达数据密集型计算。一个初始的原型编译器，这种方言的Java已经建成。我们的编译器使用静态过程间切片技术提取所需的功能，利用现有的运行时系统，主动数据存储库（ADR）。 2.声明性（基于XQL）：声明性语言的高级特性允许新计算技术的快速原型化。XML（可扩展标记语言）正在被接受为数据逻辑布局的格式，这些数据可以通过网络提供或在组织之间交换。我们将对XML模式进行扩展，以表达元素的多维集合。我们还将对XQL进行扩展，XQL是XML文档的查询语言，用于表示大型数据集的处理。 我们提出了五个重要领域的创新研究：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.用于位置管理的组合式定位器和定位器技术。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.