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Partitioned Scheduling for Real-Time Tasks on Multiprocessor Embedded Systems with Programmable Shared SRAMs

机译:具有可编程共享SRAM的多处理器嵌入式系统上实时任务的分区计划

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This work is motivated by the advance of multiprocessor system architecture, in which the allocation of tasks over heterogeneous memory modules has a significant impact on the task execution. By considering two different types of memory modules with different access latencies, this paper explores joint considerations of memory allocation and real-time task scheduling to minimize the maximum utilization of processors of the system. For implicit-deadline sporadic tasks, a two-phase algorithm is developed, where the first phase determines memory allocation to derive a lower bound of the maximum utilization, and the second phase adopts worst-fit partitioning to assign tasks. It is shown that the proposed algorithm leads to a tight (2 -(2/(M+1)))-approximation bound where M is the number of processors. The proposed algorithm is then evaluated with 82 realistic benchmarks from MRTC, Medi-aBench, UTDSP, NetBench and DSPstone, and extensive simulations are further conducted to analyze the proposed algorithm.
机译:这项工作是由多处理器系统体系结构的发展推动的,在该体系结构中,任务在异构存储模块上的分配对任务执行有重大影响。通过考虑具有不同访问延迟的两种不同类型的内存模块,本文探索了内存分配和实时任务调度的共同考虑因素,以最大程度地降低系统处理器的最大利用率。对于隐式截止的零星任务,开发了一种两阶段算法,其中第一阶段确定内存分配以得出最大利用率的下限,第二阶段采用最不适合的分区来分配任务。结果表明,所提出的算法导致了紧密的(2-(2 /(M + 1)))逼近界,其中M是处理器数量。然后使用来自MRTC,Medi-aBench,UTDSP,NetBench和DSPstone的82个现实基准对所提出的算法进行评估,并进一步进行了广泛的仿真以分析所提出的算法。

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