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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems
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Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems

机译：快速高效的基于能量梯度的调度算法异构嵌入式多处理器系统设计

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摘要

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.
机译：在本文中，我们提出了两种启发式能量感知调度算法（EGMS和EGMSIV），用于在具有处理元件的嵌入式多处理器系统中调度任务优先级图，该处理元件具有动态电压缩放功能。与大多数考虑任务排序和电压缩放与任务映射分开考虑的节能计划算法不同，我们的算法以集成方式考虑它们。 EGMS使用能量梯度的概念来选择要映射到新处理器和电压水平的任务。 EGM-SIV通过使用线性编程（LP）公式引入任务内电压缩放来扩展EGMS，以进一步降低能耗。通过严格的仿真，我们将所提出算法的性能与文献中提出的几种方法进行了比较。结果表明，我们的算法能够以更少的优化时间获得节能计划。平均而言，与文献中介绍的几种方法相比，我们的算法所产生的调度消耗的能源少10％，优化时间减少47％以上。特别是，我们的算法在生成大型任务图的节能计划方面表现更好。我们的结果表明，与其他方法相比，大型任务图的能源消耗最多减少57％。

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