Collaborative Research: Effective Detection and Alleviation of Scalability Problems

协作研究：有效检测和缓解可扩展性问题

• 批准号: 0429653
• 负责人: Frank Mueller
• 金额: --
• 依托单位: North Carolina State University
• 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: 2004-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0429653&HistoricalAwards=false
• 关键词: Collaborative; Research; Effective; Detection; Alleviation

The focus of this research proposal is to develop tool support to provide the ability for scientific programmers to inquire about scalability problems and correlate this information back to source code. Furthermore, we believe that tools should be able to suggest and evaluate optimizing transformations to alleviate these problems. This would constitute a significant improvement over current performance analysis practice.The key intellectual merit is in providing an automatic framework for detecting scalability problems andcorrelating them back to source code. We will experiment with our framework on the ASCI codes, whichis intended to stress high-performance clusters.The broader impact of this work is in three main areas. First, both PIs are working to create an interdisciplinary educational and research program. Second, students will be educated in high-performancecomputing. Finally, the proposed work allows for technology transfer to a wide arena of emerging fields,such as cluster computing as well as the established areas of SMPs and massively parallel computing.The developed framework and tools will be made generally available to the research community and high-performance computing labs.

该研究提案的重点是开发工具支持，为科学程序员提供查询可扩展性问题并将这些信息与源代码相关联的能力。此外，我们认为工具应该能够建议和评估优化转换以缓解这些问题。这将是对当前性能分析实践的重大改进。关键的智力优点是提供一个自动框架来检测可扩展性问题并将其与源代码相关联。我们将在 ASCI 代码上试验我们的框架，该框架旨在强调高性能集群。这项工作的更广泛影响体现在三个主要领域。首先，两位 PI 都致力于创建一个跨学科的教育和研究项目。其次，学生将接受高性能计算方面的教育。最后，所提出的工作允许将技术转移到新兴领域的广泛领域，例如集群计算以及 SMP 和大规模并行计算的既定领域。开发的框架和工具将普遍提供给研究界和高级研究人员。 -性能计算实验室。