High Performance Software Components for Scientific Computing

用于科学计算的高性能软件组件

• 批准号: 9982205
• 负责人: Andrew Lumsdaine
• 金额: $ 28.85万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2001-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9982205&HistoricalAwards=false
• 关键词: Performance; Software; Components; Scientific; Computing

This project will advance the state of the art of high performance computing by the refinement and application of software component technologies. The project will use these technologies to build a high-performance library of generic components with the performance and flexibility required for complex scientific applications. Three technical thrusts will support this approach. The researchers will develop and extend generic programming analysis and design methodologies to create component taxonomies for scientific computing, particularly for denoting higher-level domain abstractions and formally specifying their interfaces. Generative optimization will exploit these abstractions to automate performance tuning and optimization, thus avoiding the traditional loss of computational efficiency from high-level abstractions. Finally, library development will create highly parameterized components that offer maximum functionality and customizability for areas such as numerical linear algebra, multi-dimensional arrays, tensor operations, and stencil operations. Through the use of generative techniques, the library will be highly optimizable and will offer high performance at a reasonable development cost. Through the use of generic programming techniques, the library will incorporate parallel processing capabilities.

该项目将通过改进和应用软件组件技术来推进高性能计算技术的发展。该项目将使用这些技术来构建具有复杂科学应用所需的性能和灵活性的通用组件的高性能库。三个技术要点将支持这种方法。研究人员将开发和扩展通用编程分析和设计方法，为科学计算创建组件分类法，特别是用于表示更高级别的领域抽象和正式指定其接口。生成优化将利用这些抽象来自动进行性能调优和优化，从而避免传统上由于高级抽象而导致的计算效率损失。最后，库开发将创建高度参数化的组件，为数值线性代数、多维数组、张量操作和模板操作等领域提供最大的功能和可定制性。通过使用生成技术，库将是高度可优化的，并将以合理的开发成本提供高性能。通过使用泛型编程技术，该库将合并并行处理能力。