Tools for the Development of Memory-Efficient Sparse Linear Solvers

用于开发内存高效稀疏线性求解器的工具

• 批准号: 0430646
• 负责人: Elizabeth Jessup
• 金额: $ 25万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430646&HistoricalAwards=false
• 关键词: Tools; Development; Memory; Efficient; Sparse

Tools for the Development of Memory-Efficient Sparse Linear SolversRecent advances in both hardware and algorithm development have increasedthe effect of memory access on the runtime of large scale linear algebraapplications. The PI proposes to create tools that will provide the codedeveloper with information about memory costs of codes in preparationas well as with software building blocks designed to lessen those costs.First, a framework for memory-efficient implementation of the sequences ofsparse Basic Linear Algebra Subprograms that typically constitute a sparselinear solver will be provided. Second, a collection of metrics for theprediction of memory usage of linear algebra software will be developed.Working from those metrics, a tool to automate the prediction processwill be built. Finally, keeping in mind that the performance of a codeultimately depends on how it compiles, a comprehensive study of compilersand the performance of the executables they produce will be carried out.

开发高效内存稀疏线性求解器的工具硬件和算法开发的最新进展增加了内存访问对大规模线性代数应用程序运行时的影响。 PI建议创建工具，将提供代码开发者与信息的内存成本的代码在简化以及软件构建块，旨在减少这些costs.First，一个框架的内存效率的实现序列ofsparse基本线性代数子程序，通常构成一个sparselinear求解器将提供。 第二，我们将开发一套线性代数软件内存使用预测的度量标准，并根据这些度量标准构建一个自动化预测过程的工具。 最后，请记住，代码的性能最终取决于它如何编译，将对编译器及其产生的可执行文件的性能进行全面研究。