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

极端规模计算给并行程序设计带来了许多新的挑战，其中一个计算可能涉及数十万个具有多级并行性的进程。这种大规模并行程序的调试是非常困难的。可伸缩和轻量级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，以便在新兴的异构和极端规模计算平台上高效地分析并行程序并检测潜在错误。具体来说，研究的目标是：(1)开发仪器工具和优化的监控系统，以支持构建错误检测工具；(2)设计各种优化策略和技术，以提高可扩展性和减少开销；(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准确和有效的检测技术；(5)研究特定领域的错误检测和优化技术。这项研究将极大地帮助科学计算的极端规模并行程序的发展，并在早期发现难以发现的错误。它将大大减少繁琐的调试活动的负担，使研究人员可以专注于科学问题。该工具包的目标是通用计算平台，从本地集群到极端规模的超级计算机。在教育推力方面，研究成果将促进新课程的开发和现有课程的完善。高中生、本科生和研究生将有机会参与研究。

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.