异步多任务编程模型中的分布式软件弹性研究

Nikunj Gupta, J. Mayo, Adrian S. Lemoine, Hartmut Kaiser
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引用次数: 2

摘要

由于硬件故障而在关键任务应用程序中发生的异常和错误具有很高的成本。随着下一代平台(NGPs)的出现,硬件故障率可能会增加。因此,设计具有弹性的应用程序是一个关键问题,以便在满足功率预算限制的同时保持结果的可靠性。本文从本地和分布式两方面讨论了自动传输系统的软件弹性。我们选择HPX作为弹性设计的原型。我们实现了两个向应用程序开发人员公开的弹性api,即任务复制和任务重播。任务复制将任务重复n次,并异步执行它们。任务重放最多可重调度n次任务,直到返回有效输出。此外,我们使用用户提供的谓词(例如校验和)公开基于算法的容错(ABFT)来验证返回的结果。我们对本地和分布式规模的合成应用程序和实际应用程序的弹性方案进行了基准测试,结果表明,大部分增加的执行时间来自任务的重放、复制或数据移动,而不是为实现弹性而添加的样板代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Distributed Software Resilience in Asynchronous Many- Task Programming Models
Exceptions and errors occurring within mission critical applications due to hardware failures have a high cost. With the emerging Next Generation Platforms (NGPs), the rate of hardware failures will likely increase. Therefore, designing our applications to be resilient is a critical concern in order to retain the reliability of results while meeting the constraints on power budgets. In this paper, we discuss software resilience in AMTs at both local and distributed scale. We choose HPX to prototype our resiliency designs. We implement two resiliency APIs that we expose to the application developers, namely task replication and task replay. Task replication repeats a task n-times and executes them asynchronously. Task replay reschedules a task up to n-times until a valid output is returned. Furthermore, we expose algorithm based fault tolerance (ABFT) using user provided predicates (e.g., checksums) to validate the returned results. We benchmark the resiliency scheme for both synthetic and real world applications at local and distributed scale and show that most of the added execution time arises from the replay, replication or data movement of the tasks and not the boilerplate code added to achieve resilience.
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