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Exact Response Time Analysis for Fixed Priority Memory-Processor Co-Scheduling

机译:固定优先级存储器-处理器协同调度的精确响应时间分析

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Recent technological advances have led to an increasing gap between memory and processor performance, since memory bandwidth is progressing at a much slower pace than processor bandwidth. Pre-fetching techniques are traditionally used to bridge this gap and achieve high processor utilization while tolerating high memory latencies. Following this trend, new computational models have been proposed to split task execution in two consecutive phases: a memory phase in which the required instructions and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. This paper takes a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the memory-computation (M/C) model, by proposing a theoretical framework aimed at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on single-core systems. In particular, a critical instant is identified for M/C tasks scheduled with fixed priority and an exact response time analysis with pseudo-polynomial complexity is provided. Then, we investigate the problem of priority assignment for M/C tasks, showing that a necessary condition to achieve optimality is to allow different priorities for the two phases. Our experiments show that the proposed techniques provide a significant schedulability improvement with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems.
机译:最近的技术进步导致内存和处理器性能之间的差距越来越大,因为内存带宽的发展速度比处理器带宽要慢得多。传统上,预取技术用于弥合此差距并在允许高内存延迟的同时实现较高的处理器利用率。遵循这一趋势,已经提出了新的计算模型,以将任务执行分为两个连续的阶段:一个存储阶段,其中所需的指令和数据被预取到本地存储器(M阶段);以及一个执行阶段,其中任务在没有存储器争用的情况下执行(C阶段)。将存储器和执行阶段解耦不仅可以简化时序分析,而且还可以通过适当的协同调度算法对存储器和执行阶段进行更有效(且可预测)的流水线处理。本文提出了一个理论框架,旨在紧密地描述可采用的可实现的可调度性改进的理论框架,从而进一步设计了适用于符合内存计算(M / C)模型的零星实时任务的智能协同调度算法。单核系统上的M / C任务模型。特别地,针对以固定优先级调度的M / C任务识别关键时刻,并且提供具有伪多项式复杂度的精确响应时间分析。然后,我们调查了M / C任务的优先级分配问题,表明实现最佳化的必要条件是允许两个阶段具有不同的优先级。我们的实验表明,相对于经典执行模型,所提出的技术提供了显着的可调度性改进,为设计更有效的分区多核系统奠定了重要的基础。

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