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Analyzing Data Cache Related Preemption Delay With Multiple Preemptions

机译:分析具有多个抢占的数据缓存相关抢占延迟

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Timing analysis of real-time tasks under preemptive scheduling must take cache-related preemption delay (CRPD) into account. Typically, a task may be preempted more than once during the execution in each period. To bound the total CRPD of k preemptions, existing CRPD analysis techniques estimate the CRPD at each program point, and use the sum of the k-largest CRPD among all program points as the total CRPD upper bound. In this paper, we disclose that the above-mentioned approach, although works well for instruction caches, leads to significant overestimation when dealing with data caches. This is because on data caches, the CRPD of preemptions at different program points may have correlations, and the total CRPD of multiple preemptions is in general smaller than the simple sum of the worst-case CRPD of each preemption. To address this problem, we propose a new technique to efficiently explore the correlation among the CRPD of different preemptions, and thus more precisely calculate the total CRPD. Experiments with benchmark programs show that the proposed technique leads to substantially tighter total CRPD estimation with multiple preemptions comparing with the state-of-the-art.
机译:抢占式调度下的实时任务的时序分析必须考虑与缓存相关的抢占延迟(CRPD)。通常,在每个周期的执行过程中,任务可能会被抢占多次。为了限制k个抢占的总CRPD,现有的CRPD分析技术会估算每个程序点的CRPD,并将所有程序点中k个最大CRPD的总和用作总CRPD上限。在本文中,我们公开了上述方法尽管对指令缓存有效,但在处理数据缓存时会导致明显的高估。这是因为在数据高速缓存中,不同程序点处的抢占的CRPD可能具有相关性,并且多个抢占的总CRPD通常小于每个抢占的最坏情况的CRPD的简单总和。为了解决这个问题,我们提出了一种新技术,可以有效地探索不同优先权的CRPD之间的相关性,从而更精确地计算总CRPD。与基准程序进行的实验表明,与最新技术相比,所提出的技术可以通过多次抢占来大幅提高总的CRPD估算。

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