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

DC：媒介：协作研究：ELLF：用于可扩展且高效的数据密集型应用程序的可扩展语言和库框架

• 批准号: 0905205
• 负责人: Alok Choudhary
• 金额: $ 41万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905205&HistoricalAwards=false
• 关键词: DC; Medium; Collaborative; Research; ELLF

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.

科学数据集对petabyte尺寸的增长为燃烧化学，纳米科学，天体物理学，气候预测和生物学以及互联网数据等领域的重要发现提供了重要的机会。但是，由于缺乏易于使用的编程模型和工具，从这些数据中实现新科学见解的限制受到创建可扩展应用程序的困难。为了应对创建数据密集应用程序的挑战，该项目将建立一个可扩展的语言框架，并获得高性能库（I/O和分析）的表现力集合，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使程序员和科学家可以更轻松地更轻松地更轻松地将其直接指定为编写domain-domain-domain-adaDAdAdAdapAdapatedAptaptedaptedaptedaptedaptedaptated odadaptedaptedaptedapted odadaptedapted odadAdaptedaptedaptedaptedapted odedapted odadAdapticages。该项目将基于最近属性的语法研究，以建立C向主机特定领域的语言扩展，这也将解决储存，I/O和低级语言中的分析能力的不足。C。诸如C。诸如所提出的扩展语言和图书馆框架的可能性解决方面的问题的潜在范围有效的问题，以允许有效地解决程序，以自来可以解决问题。高级抽象。最终的语言框架和库将为研究人员免费提供，以编写涉及时空数据的气候和其他应用程序。这包括物理科学和工程中的许多应用程序，因此预计该框架也会在其他科学领域中找到使用。