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

科学数据集的增长到PB大小，为燃烧化学、纳米科学、天体物理、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大机会。然而，由于缺乏易于使用的编程模型和工具，创建可伸缩应用程序的难度限制了从这些数据实现新的科学见解。为了应对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，并以高性能库(I/O和分析)的富有表现力的集合为后盾，以提供一个开发环境，在该环境中，多个特定于领域的语言扩展允许程序员和科学家更容易和直接地将数据密集型问题的解决方案指定为以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，以构建C的可扩展规范来托管特定于领域的语言扩展，该规范还将解决诸如C等低级语言在存储、I/O和分析能力方面的不足。拟议的可扩展语言和库框架具有成为程序员和科学家的变革性问题解决环境的潜力，因为它允许在高抽象级别上指定针对数据密集型问题的可扩展和高效的解决方案。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其他涉及时空数据的应用程序的研究人员。这包括在物理科学和工程学方面的许多应用，因此，预计该框架也将在其他科学领域得到使用。

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.