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WCET-aware Software Based Cache Partitioning for Multi-Task Real-Time Systems

机译:基于WCET的软件基于多任务实时系统的缓存分区

摘要

Caches are a source of unpredictability since it is very difficult to predict if a memory access results in a cache hit or miss. In systems running multiple tasks steered by a preempting scheduler, it is even impossible to determine the cache behavior since interrupt-driven schedulers lead to unknown points of time for context switches. Partitioned caches are already used in multi-task environments to increase the cache hit ratio by avoiding mutual eviction of tasks from the cache.For real-time systems, the upper bound of the execution time is one of the most important metrics, called the Worst-Case Execution Time (WCET). In this paper, we use partitioning of instruction caches as a technique to achieve tighter WCET estimations since tasks can not be evicted from their partition by other tasks. We propose a novel WCET-aware cache partitioning algorithm, which determines the optimal partition size for each task with focus on decreasing the systemu27s WCET for a given set of possible partition sizes. Employing this algorithm, we are able to decrease the WCET depending on the number of tasks in a set by up to 34%. On average, reductions between 12% and 19% can be achieved.
机译:高速缓存是不可预测性的根源,因为很难预测内存访问是否会导致高速缓存命中或未命中。在运行由抢占式调度程序控制的多个任务的系统中,甚至无法确定缓存行为,因为中断驱动的调度程序导致上下文切换的未知时间点。分区缓存已经在多任务环境中使用,它通过避免从缓存中相互驱逐任务来提高缓存命中率。对于实时系统,执行时间的上限是最重要的指标之一,称为最差指标-案例执行时间(WCET)。在本文中,我们使用指令缓存分区作为一种实现更严格的WCET估计的技术,因为其他任务无法将任务从其分区中逐出。我们提出了一种新颖的可感知WCET的缓存分区算法,该算法确定每个任务的最佳分区大小,重点是针对给定的一组可能的分区大小,降低系统的WCET。使用此算法,我们可以根据集合中的任务数将WCET降低多达34%。平均而言,可以减少12%至19%。

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