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.

超大规模计算给并行程序设计带来了许多新的挑战，其中计算可能涉及具有多级并行性的数十万个进程。调试这样大规模的并行程序是非常困难的。可扩展和轻量级的正确性工具是应对这一挑战的关键。本研究旨在设计创新的算法和开发一个可扩展的工具包，以有效地分析并行程序和检测潜在的错误在新兴的异构和极端规模的计算平台。具体而言，研究的目标是：（1）开发插装工具和优化的监控系统，以支持用于错误检测的构建工具，（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）集成静态和动态程序分析，以提高报告准确性和代码覆盖率，（4）在大规模并行系统上设计更精确和有效的检测技术，（5）研究面向特定领域的错误检测和优化技术，这将有助于科学计算中超大规模并行程序的开发，并有助于发现难以实现的错误。在早期发现错误。它将大大减轻繁琐的调试活动的负担，因此研究人员可以专注于科学问题。该工具包面向通用计算平台，从本地集群到极端规模的超级计算机。在教育方面，研究成果将促进新课程的开发和加强现有课程。高中，本科和研究生将有机会参与研究。