SHF: SMALL: Ant: Automatic and Manual Debugging Support for Massively Parallel Programs

SHF：SMALL：Ant：大规模并行程序的自动和手动调试支持

• 批准号: 0916901
• 负责人: Samuel Midkiff
• 金额: $ 49.32万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916901&HistoricalAwards=false
• 关键词: SHF; SMALL; Ant; Automatic; Manual

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The research funded by this award targets the difficult problem of how to debug programs running on large parallel systems. The state of hundreds to hundreds of thousands of parallel tasks that form a single computation are too complicated for a programmer to usefully analyze. This project will develop tools to find similarities between the state of different processes, simplifying the task of the programmer. The challenge in finding these similar tasks is to do it efficiently, without imposing anoverhead so hight that the tool is useless. A naive implementation would compare the state of all processors against one another, and would introduce overheads increasing as the square of the number of processors. Our approach will successively refine sets of similar processes, will use key attributes of program behavior (e.g. communication patterns) to perform this grouping. We will also investigate the use these groups of similar processes to allow invariance and statistically based techniques developed for sequential programs (such as value and PC invariance) to be effectively adapted to parallel programs. Because these techniques look for rarely occurring programactivities, applying them to disparate processes together will introducenoise into the analysis, severely diminishing their accuracy. The use of our grouping strategy will allow effective parallelization of the techniques, allowing them to be applied with significantly less overhead than when used with sequential applications.

该奖项根据 2009 年《美国复苏和再投资法案》（公法 111-5）提供资金。 该奖项资助的研究针对的是如何调试大型并行系统上运行的程序的难题。 形成单个计算的数百到数十万个并行任务的状态对于程序员来说太复杂而无法进行有效分析。 该项目将开发工具来查找不同进程状态之间的相似性，从而简化程序员的任务。 找到这些类似任务的挑战是如何高效地完成任务，而又不会造成过高的开销以致该工具毫无用处。 一种简单的实现会将所有处理器的状态相互比较，并且会引入随着处理器数量的平方而增加的开销。 我们的方法将连续细化相似过程的集合，将使用程序行为的关键属性（例如通信模式）来执行此分组。 我们还将研究如何使用这些类似过程组，以允许为顺序程序开发的不变性和基于统计的技术（例如值和 PC 不变性）有效地适应并行程序。 由于这些技术寻找很少发生的程序活动，因此将它们一起应用于不同的进程会在分析中引入噪声，从而严重降低其准确性。 使用我们的分组策略将允许技术的有效并行化，从而使它们的应用开销比顺序应用程序使用时的开销要少得多。