CAREER: Towards Scalable Error Detection for Parallel Software Systems on Emerging Computing Platforms

职业：在新兴计算平台上实现并行软件系统的可扩展错误检测

• 批准号: 1622292
• 负责人: Liqiang Wang
• 金额: $ 25.22万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622292&HistoricalAwards=false
• 关键词: CAREER; Towards; Scalable; Error; Detection

Extreme scale computing introduces many new challenges to parallel program design, where a computation may involve hundreds of thousands of processes with multiple-level parallelism. It is very difficult to debug such large-scale parallel programs. Scalable and light-weight correctness tools are critical to combat this challenge.This research seeks to design innovative algorithms and develop a scalable toolkit to efficiently and effectively analyze parallel programs and detect potential errors on the emerging heterogeneous and extreme scale computing platforms. Specifically, the objectives of the research are to: (1) develop instrumentation tools and optimized monitoring systems to support building tools for error detection, (2) design various optimization strategies and techniques to improve scalability and reduce overhead, (3) integrate static and dynamic program analyses to improve reporting accuracy and code coverage, (4) design more accurate and efficient detection techniques on large-scale parallel systems, and (5) investigate domain-specific techniques for error detection and optimization.This research will greatly help the development of extreme scale parallel programs for scientific computing and discover hard-to-find errors in early stage. It will significantly reduce the burden of tedious debugging activities, so researchers can focus on scientific problems. The toolkit is targeted for general computing platforms, from local clusters to extreme scale supercomputers. In the education thrust, the research results will facilitate the development of new courses and enhance existing ones. High-school, undergraduate, and graduate students will have opportunities to get involved in the research.

极限规模的计算引入了并行程序设计的许多新挑战，其中计算可能涉及数十万个具有多级并行性的过程。调试这样的大规模平行程序非常困难。可扩展和轻巧的正确性工具对于应对这一挑战至关重要。本研究旨在设计创新的算法并开发可扩展的工具包，以有效，有效地分析并行程序，并在新兴的异质和极端规模的计算平台上检测潜在错误。具体而言，研究的目标是：（1）开发仪器工具和优化的监视系统，以支持构建错误检测工具，（2）设计各种优化策略和技术，以提高可伸缩性并减少开销，（3）整合静态和动态的程序分析，以整合改进报告的准确性和代码覆盖范围，并将更准确的检测和有效的技术跨度（4）跨性别技术覆盖，（4）跨度技术，（4）为了进行错误检测和优化。这项研究将极大地帮助开发极端的平行程序，以实现科学计算，并在早期发现难以确定的错误。它将大大减轻乏味的调试活动的负担，因此研究人员可以专注于科学问题。该工具包针对一般计算平台，从本地群集到极端的超级计算机。在教育方面，研究结果将有助于开发新课程并增强现有课程。高中，本科生和研究生将有机会参与研究。