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

极端规模的计算给并行程序设计带来了许多新的挑战，一次计算可能涉及数十万个具有多级并行性的进程。调试如此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可扩展的工具包，以高效和有效地分析并行程序，并在新兴的异构性和极端规模计算平台上检测潜在错误。具体来说，研究的目标是：(1)开发工具和优化的监控系统来支持错误检测工具的构建；(2)设计各种优化策略和技术来提高可伸缩性和降低开销；(3)结合静态和动态程序分析来提高报告精度和代码覆盖率；(4)在大规模并行系统上设计更准确和高效的检测技术；(5)研究特定领域的错误检测和优化技术。这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。在教育推动方面，研究成果将促进新课程的开发和现有课程的提升。高中生、本科生和研究生将有机会参与这项研究。

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.