SHF: EAGER: Platform-Agnostic Supercomputing from Scientific Metaprogramming

SHF：EAGER：来自科学元编程的与平台无关的超级计算

• 批准号: 1248464
• 负责人: Matthew Might
• 金额: $ 20万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248464&HistoricalAwards=false
• 关键词: SHF; EAGER; Platform; Agnostic; Supercomputing

Porting scientific codes to new high-performance platforms is a time consuming and error-prone process. The research funded by this EAGER award will make scientific codes more portable by raising the level of abstraction. The Principal Investigators will develop a domain-specific language embedded in C++ for solving partial differential equations. They will evaluate the effectiveness of their approach by applying their language to the problem of reacting flow simulations, which is a problem that is important for modeling zero-emission power plants.Computation is an increasingly important tool for scientists, but scientists must hand-tune their code for each new machine to take advantage of the latest high-performance hardware. This research represents a new approach to implementing scientific codes on high-performance machines. The approach allows domain experts to write code at a high level of abstraction while gaining portability across parallel platforms. This research has the potential to improve the productivity of computational scientists by transforming the way that scientific codes are developed.

将科学代码移植到新的高性能平台是一个耗时且容易出错的过程。 EAGER奖资助的研究将通过提高抽象水平使科学代码更具可移植性。 主要研究人员将开发一种嵌入C++的特定领域语言，用于求解偏微分方程。 他们将通过将他们的语言应用于反应流模拟问题来评估他们的方法的有效性，这是一个对零排放发电厂建模非常重要的问题。计算对科学家来说是一个越来越重要的工具，但科学家必须为每台新机器手动调整代码，以利用最新的高性能硬件。 这项研究代表了在高性能机器上实施科学代码的新方法。 该方法允许领域专家在高抽象级别上编写代码，同时获得跨并行平台的可移植性。 这项研究有可能通过改变科学代码的开发方式来提高计算科学家的生产力。