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Hybrid Scheduling of Dynamic Task Graphs with Selective Duplication for Multiprocessors under Memory and Time Constraints

机译:内存和时间约束下具有选择性复制的多处理器动态任务图的混合调度

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This paper presents a hybrid scheduling methodology for task graphs to multiprocessor embedded systems. The proposed methodology is designed for task graphs which are dynamic in nature due to the presence of conditional tasks as well as tasks whose execution times are unpredictable but bounded. We have presented the methodology as a three phase strategy in which task nodes are mapped to the processors in the first (static mapping) phase. In the second (selective duplication) phase some critical nodes are identified and duplicated for possible rescheduling at run-time depending on the code memory constraints of the processors. The third (online) phase is a run-time scheduling algorithm that performs list scheduling based on actual dynamics of the schedule up to the current time. We show that this technique provides better schedule length (up to 20%) compared to previous techniques which are predominantly static in nature with low overhead and comparable in complexity with existing online techniques. The effects of model parameters like number of processors, memory and various task graph parameters on performance are investigated in this paper.
机译:本文提出了一种用于多处理器嵌入式系统任务图的混合调度方法。所提出的方法是为任务图而设计的,由于条件任务的存在以及执行时间不可预测但有界的任务,任务图本质上是动态的。我们将方法论作为一种三阶段策略进行了介绍,其中任务节点在第一(静态映射)阶段被映射到处理器。在第二(选择性复制)阶段中,根据处理器的代码存储约束,在运行时可能会进行一些重新调度以识别并复制一些关键节点。第三(在线)阶段是运行时调度算法,该算法根据直到当前时间的调度的实际动态执行列表调度。我们显示,与以前的技术相比,该技术提供了更好的调度长度(最多20%),而以前的技术本质上是静态的,具有较低的开销,并且在复杂性上与现有的在线技术相当。本文研究了诸如处理器数量,内存和各种任务图参数之类的模型参数对性能的影响。

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