A survey on checkpointing strategies: Should we always checkpoint à la Young/Daly?

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
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引用次数: 0

Abstract

The Young/Daly formula provides an approximation of the optimal checkpointing period for a parallel application executing on a supercomputing platform. It was originally designed to handle fail-stop errors for preemptible tightly-coupled applications, but has been extended to other application and resilience frameworks. We provide some background and survey various scenarios to assess the usefulness and limitations of the formula, both for preemptible applications and workflow applications represented as a graph of tasks. We also discuss scenarios with uncertainties, and extend the study to silent errors. We exhibit cases where the optimal period is of a different order than that dictated by the Young/Daly formula, and finally we explain how checkpointing can be further combined with replication.

关于检查点策略的调查:我们是否应该总是像 Young/Daly 一样设置检查点?
Young/Daly 公式为在超级计算平台上执行的并行应用程序提供了最佳检查点周期的近似值。该公式最初是为处理可抢占式紧耦合应用程序的故障停止错误而设计的,现已扩展到其他应用程序和弹性框架。我们介绍了一些背景情况,并对各种场景进行了调查,以评估该公式的实用性和局限性,既适用于可抢占式应用,也适用于以任务图表示的工作流应用。我们还讨论了具有不确定性的方案,并将研究扩展到了无声错误。我们展示了最佳周期与 Young/Daly 公式所规定的周期不同的情况,最后我们解释了如何将检查点与复制进一步结合起来。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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