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.

用于开发硬件和算法开发中记忆效率稀疏线性求解进展的工具增加了内存访问对大型线性代数应用程序运行时的影响。 PI提议创建工具，以为编码开发人员提供有关准备人员中代码的内存成本的信息，以及旨在降低这些成本的软件构建块。首先，这是用于实现Sparse基本线性代数序列的记忆效率框架，通常将提供A sparseleareareareareareareareareareareal lorter sprogram。 其次，将开发用于预测线性代数软件内存使用的指标集合。从这些指标开始，可以构建预测过程的工具。 最后，请记住，编码的性能取决于其编译的方式，将对编译者进行全面研究，并将执行其生产的可执行文件的性能。