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Energy-efficient Real-time Scheduling on Multicores
ACM Transactions on Embedded Computing Systems ( IF 2 ) Pub Date : 2020-07-04 , DOI: 10.1145/3399413
Saad Zia Sheikh 1 , Muhammad Adeel Pasha 1
Affiliation  

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 counter-measure 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.

中文翻译:

多核上的节能实时调度

随着对更高性能的需求不断增加,多核的采用已成为硬实时系统发展的主要垫脚石。尽管并行处理增加了计算带宽,但分层内存子系统与多核之间不可或缺的交互性进一步加剧了已经复杂的任务最坏情况执行时间(WCET)分析。此外,缓存对任务执行时间的影响最大,共享缓存的加入进一步增加了系统的不可预测性。已经提出缓存分区技术作为将共享缓存延迟与 WCET 分离的对策。然而,现有的节能调度算法忽略了共享缓存或缓存分区技术的不可预测性,因此,降低了它们对现实世界系统的适用性。在不考虑任务间缓存争用的情况下,直接使用现有算法或尝试分配和调度具有缓存分区分配的任务集可能会导致缓存冲突。为了克服这个困境,我们提出了一种新的方法来将任务间缓存争用建模为依赖图,供成熟的算法使用以最小化能耗。广泛的模拟证明了我们的方法在最大限度地减少能源消耗同时避免缓存违规的有效性。我们提出了一种新颖的方法来将任务间缓存争用建模为依赖图,供成熟的算法使用以最小化能耗。广泛的模拟证明了我们的方法在最大限度地减少能源消耗同时避免缓存违规的有效性。我们提出了一种新颖的方法来将任务间缓存争用建模为依赖图,供成熟的算法使用以最小化能耗。广泛的模拟证明了我们的方法在最大限度地减少能源消耗同时避免缓存违规的有效性。
更新日期:2020-07-04
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