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

可靠性已经成为许多计算机系统的关键必要属性，这些计算机系统围绕着我们或在幕后支持我们的个人和职业生活。我们在设计、实现和操作可靠系统的能力方面取得了长足的进步。然而，由于计算系统规模的快速增长，可靠性解决方案越来越受到重视。在计算基因组学、数据挖掘和自然现象预测等领域中使用的计算机应用程序解决了产生大量传感数据的极其复杂的问题;因此，这些应用程序的输入是巨大的。 计算正在迅速变得更加依赖于并行性-其中许多计算同时进行。这意味着增加服务器的核心数量，更多的服务器和数据中心机架，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根本不起作用。当应用程序未完成或完成时结果不正确时，开发人员必须确定有问题的并行任务，然后确定该任务中导致错误的代码部分。这对于小到中等规模的并行应用程序来说已经足够困难了。这些问题在大范围内加剧。几十个进程的处理对于普通的开发人员来说是触手可及的，几百个进程的处理对于英勇的开发人员来说也是触手可及的，但是在千万亿次甚至更大规模的机器上，这需要复杂的支持。这个项目将为调试工具创建设计原则，这些工具可以在大规模的数据和进程计数下运行，并在一个名为LANCET的系统中实际实例化这些原则。该方法将基于这样的见解，即应用程序中的进程的等价类的数量不会随着进程数量的增长而增长。 分析将主要处理等价类。Resilience运行时将包含以完全分布式的方式对各个进程进行操作的元素。在需要非当地知识的情况下，这些技术将以抽样方式运作。最后，该项目将为数据依赖性错误制定解决方案，这些错误无法令人信服地广泛适用于解决方案，即，对于特定的输入数据集或特定的输入参数组合表现出的错误类型。

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.