DC: Medium: Collaborative Research: ELLF: Extensible Language and Library Frameworks for Scalable and Efficient Data-Intensive Applications

科学数据集增长到PB级，为燃烧化学、纳米科学、天体物理学、气候预测和生物学等领域的重要发现以及互联网上的数据提供了重要机会。 然而，由于缺乏易于使用的编程模型和工具，难以创建可扩展的应用程序，因此从这些数据中实现新的科学见解受到限制。为了解决创建数据密集型应用程序的挑战，该项目将建立一个可扩展的语言框架，由高性能库（I/O和分析）的表达集合支持，以提供一个开发环境，在该环境中，多个特定领域的语言扩展允许程序员和科学家更容易和直接地指定数据密集型问题的解决方案，作为用适应领域的语言编写的程序。 该项目将建立在最近的属性语法研究的基础上，建立一个可扩展的C规范，以托管特定领域的语言扩展，这也将解决低级语言（如C）在存储，I/O和分析能力方面的性能不足。拟议的可扩展语言和库框架有可能成为一个变革性的问题解决环境的程序员和科学家，因为它允许可扩展的和有效的解决方案，以数据密集型的问题，以指定在一个高层次的抽象。 由此产生的语言框架和库将免费提供给编写气候应用程序和涉及时空数据的其他应用程序的研究人员。 这包括物理科学和工程学中的许多应用，因此预计该框架也将在其他科学领域中使用。

The growth of scientific data sets to petabyte sizes offers significant opportunities for important discoveries in fields such as combustion chemistry, nanoscience, astrophysics, climate prediction and biology as well as from data on the internet. However, the realization of new scientific insights from this data is limited by the difficulty of creating scalable applications due to the lack of easy-to-use programming models and tools. To address challenges in creating data intensive applications, the project will build an extensible language framework, backed by an expressive collection of high-performance libraries (I/O and analytic), to provide a development environment in which multiple domain-specific language extensions allow programmers and scientists to more easily and directly specify solutions to data-intensive problems as programs written in domain-adapted languages. The project will build on recent attribute grammar research to build an extensible specification of C to host domain-specific language extensions which will also address the inadequate performance in storage, I/O and analysis capabilities in low-level language such as C. The proposed extensible language and library framework has the potential to be a transformative problem solving environment for programmers and scientists since it allows scalable and efficient solutions to data-intensive problems to be specified at a high-level of abstraction. The resulting language framework and libraries will be freely available to researchers writing applications for climate and other applications involving spatio-temporal data. This includes many applications in the physical sciences and engineering and thus it is expected that the framework will find use in other scientific domains as well.