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首页> 外文期刊>ACM Transactions on Embedded Computing Systems >Energy-efficient Real-time Scheduling on Multicores: A Novel Approach to Model Cache Contention
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Energy-efficient Real-time Scheduling on Multicores: A Novel Approach to Model Cache Contention

机译:多设备上节能实时调度:一种模型缓存争用的新方法

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With the increasing demand for higher performance, the adoption of multicores has been a major stepping stone in the evolution of hard real-time systems. Though the computational bandwidth is increased due to parallel processing, the indispensable interactivity between the hierarchical memory sub-system and multiple cores has further aggravated the already complex worst case execution time (WCET) analysis of tasks. Furthermore, caches have the biggest influence on task execution time, and the inclusion of shared caches further increases the unpredictability of the system. Cache partitioning techniques have been proposed as a countermeasure to decouple the shared cache latency from the WCET. However, existing energy-efficient scheduling algorithms are oblivious to the unpredictable nature of shared caches or cache partitioning techniques, thus, diminishing their applicability to real-world systems. Without considering inter-task cache contention, directly using existing algorithms or attempting to allocate and schedule a taskset with cache-partition assignments can result in cache violations. To overcome this dilemma, we propose a novel approach to model inter-task cache contention as a dependency graph to be used by well-established algorithms to minimize energy consumption. Extensive simulations demonstrate the effectiveness of our approach to minimize energy consumption while also avoiding cache violations.
机译:随着对更高性能的需求日益增加,多理机的采用是硬实时系统的演变中的主要踏脚石。尽管由于并行处理而增加计算带宽,但是分层存储器子系统和多个核之间的不可缺少的交互性进一步加剧了已经复杂的最坏情况执行时间(WCET)的任务分析。此外,缓存对任务执行时间最大的影响,并包含共享高速缓存进一步提高了系统的不可预测性。已经提出了缓存分区技术作为与WCET分离共享缓存延迟的对策。然而,现有的节能调度算法对共享高速缓存或高速缓存分区技术的不可预测性质,因此,减少了它们对现实世界系统的适用性。如果不考虑任务间缓存争用,直接使用现有算法或尝试分配和调整具有缓存分区分配的Taskset可以导致缓存违规。为了克服这种困境,我们提出了一种新颖的方法来模拟任务间高速缓存争用,作为良好的算法来利用的依赖性图来最小化能量消耗。广泛的模拟展示了我们方法可以最大限度地减少能源消耗的效果,同时也避免了缓存违规。

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