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ARTE: An Application-specific Run-Time managEment framework for multi-cores based on queuing models

机译:ARTE:基于排队模型的多核应用程序特定的运行时管理框架

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This paper presents an Application-specific Run-Time managEment (ARTE) framework to tackle the problem of managing computational resources in an application specific multi-core system. The ARTE framework run-time goal is to minimize applications' response times while meeting the applications' computational demands and fitting within the available power budget. The approach addresses application specific embedded systems assuming that the set of target applications is known at design-time. In addition, it considers run-time scenarios that are unpredictable due to variable user activity and/or interaction with the external environment. ARTE takes decisions about resource distribution to the active applications at run-time once the system state is known. ARTE leverages an analytical queuing model at run-time to predict the applications' response times. The accuracy of this model is enhanced by accounting for contention overhead on the resources shared among the active applications. The analytical nature of the queuing model allows an estimation of the system performance with negligible overhead. Finally we compared the proposed ARTE framework to state-of-the-art techniques to assess its benefits in terms of systems performance and run-time overhead for the selected set of parallel benchmarks.
机译:本文提出了一种特定于应用程序的运行时管理(ARTE)框架,以解决在特定于应用程序的多核系统中管理计算资源的问题。 ARTE框架的运行时目标是在满足应用程序的计算需求并符合可用功率预算的情况下,最大限度地减少应用程序的响应时间。假设目标应用程序集在设计时是已知的,则该方法可解决特定于应用程序的嵌入式系统。此外,它还考虑了由于可变的用户活动和/或与外部环境的交互而无法预测的运行时方案。一旦知道系统状态,ARTE就会在运行时做出有关将资源分配给活动应用程序的决策。 ARTE在运行时利用分析排队模型来预测应用程序的响应时间。通过考虑活动应用程序之间共享的资源上的争用开销,可以提高此模型的准确性。排队模型的分析性质允许以可忽略的开销估算系统性能。最后,我们将拟议的ARTE框架与最新技术进行了比较,以评估针对所选并行基准组的系统性能和运行时开销方面的优势。

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