Collaborative Research: Efficient Detection and Alleviation of Scalability Problems

这项研究计划的重点是开发工具支持，为科学程序员提供查询可伸缩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。关键的智能优点是提供了一个自动框架，用于检测可伸缩性问题并将它们与源代码关联起来。我们将在ASCI代码上测试我们的框架，该代码旨在强调高性能集群。这项工作的更广泛影响主要体现在三个方面。首先，两家私人投资机构都在努力创建一个跨学科的教育和研究计划。其次，学生将接受高性能计算机方面的教育。最后，拟议的工作允许将技术转移到广泛的新兴领域，如集群计算以及SMPS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性能计算实验室。

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.