CSR: Small: Diagnosing Performance and Correctness Errors in Parallel Applications at Large Scales

CSR：小：诊断大规模并行应用程序中的性能和正确性错误

• 批准号: 1527262
• 负责人: Saurabh Bagchi
• 金额: $ 45万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527262&HistoricalAwards=false
• 关键词: CSR; Small; Diagnosing; Performance; Correctness

Dependability has become a critically necessary property for many of the computer systems that surround us or work behind the scenes supporting our personal and professional lives. We have made great strides in our ability to design, implement, and operate dependable systems. However, dependability solutions are increasingly being stressed due to rapid increases in the scale of the computing systems. Computer applications used in areas such as computational genomics, data mining, and prediction of natural phenomena tackle extremely complex problems which generate vast amounts of sensory data; thus, the inputs to these applications is tremendous. Computing is rapidly becoming more dependent on parallelism - where many calculations are carried out simultaneously. This means increasing core counts for servers, more servers and racks for data centers, and a dramatic increase in the number of computing cores that these applications must run. The traditional dependability solutions just are not working. When an application does not complete or completes with incorrect results, the developer must identify the offending parallel task and then the portion of the code in that task that caused the error. This is hard enough for parallel applications at small to moderate sizes. These issues get exacerbated at large scales. Dealing with tens of processes is within reach of mere mortal developers, a few hundreds of processes is within reach of heroic developers, but on machines of petascale and beyond, this requires sophisticated support.This project will create design principles for debugging tools that can operate at large scales of data and process count and a practical instantiation of these principles in a system called LANCET. The methodology will be based on the insight that the numbers of equivalence classes of processes in an application do not grow even as the number of processes grows. Analysis will mostly deal with equivalence classes. Resilience runtime will have elements that operate on individual processes in a completely distributed manner. Where non-local knowledge is needed, the techniques will operate in a sampling mode. Finally, the project will develop solutions for data-dependent errors that have resisted convincing widely applicable solutions, i.e., errors of the kind that manifest themselves for specific input datasets or specific input parameter combinations.

对于围绕我们的许多计算机系统或在幕后工作以支持我们的个人和职业生活的许多计算机系统，可靠性已成为至关重要的属性。我们在设计，实施和操作可靠的系统的能力方面取得了长足的进步。但是，由于计算系统规模的快速增加，可靠性解决方案越来越多地受到压力。在计算基因组学，数据挖掘和自然现象的预测等领域使用的计算机应用解决了极其复杂的问题，这些问题产生了大量的感官数据；因此，这些应用程序的输入是巨大的。 计算正迅速依赖于并行性 - 同时进行许多计算。这意味着增加了服务器的核心计数，更多的数据中心的服务器和机架，以及这些应用程序必须运行的计算核心数量的急剧增加。传统的可靠性解决方案只是行不通的。当应用程序未完成或以不正确的结果完成时，开发人员必须确定有问题的并行任务，然后确定导致错误的该任务中代码的部分。对于小到中等尺寸的并行应用来说，这很难。这些问题在很大的规模上加剧了。仅仅是致命的开发人员就可以接触到数十万种流程，几百个流程可以触及英雄开发人员，但是在Petascale及其他方面的机器上，这需要复杂的支持。该项目将创建可以调试工具的设计原理，这些工具可以在数据和流程数量和流程计数和这些原理的实际实例化的系统中进行调用。该方法将基于以下见解：即使过程数量的数量增加，应用程序中的等效类别的流程类别也不会增长。 分析主要涉及等效类。弹性运行时将以完全分布的方式在单个流程上运行的元素。在需要非本地知识的地方，该技术将以采样模式运行。最后，该项目将开发有关数据依赖性错误的解决方案，这些错误拒绝了可说服广泛适用的解决方案，即为特定输入数据集或特定的输入参数组合所表现出的那种错误。