A scenario-based run-time task mapping algorithm for MPSoCs

W. Quan, A. Pimentel
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引用次数: 44

Abstract

The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such systems which could cause serious changes in the intensity and nature of the workload demands over time. To cope with the dynamism of application workloads at run time and improve the efficiency of the underlying system architecture, this paper presents a novel scenario-based run-time task mapping algorithm. This algorithm combines a static mapping strategy based on workload scenarios and a dynamic mapping strategy to achieve an overall improvement of system efficiency. We evaluated our algorithm using a homogeneous MPSoC system with three real applications. From the results, we found that our algorithm achieves an 11.3% performance improvement and a 13.9% energy saving compared to running the applications without using any run-time mapping algorithm. When comparing our algorithm to three other, well-known run-time mapping algorithms, it is superior to these algorithms in terms of quality of the mappings found while also reducing the overheads compared to most of these algorithms.
基于场景的mpsoc运行时任务映射算法
在现代基于mpsoc的嵌入式系统中,应用程序工作负载变得越来越动态。在这样的系统中,不同的应用程序并发地执行并争夺资源,这可能会导致工作负载需求的强度和性质随着时间的推移发生严重变化。为了应对运行时应用工作负载的动态性,提高底层系统架构的效率,本文提出了一种基于场景的运行时任务映射算法。该算法结合了基于工作负载场景的静态映射策略和动态映射策略,实现了系统效率的整体提升。我们使用一个具有三个实际应用的同构MPSoC系统来评估我们的算法。从结果中,我们发现与不使用任何运行时映射算法运行应用程序相比,我们的算法实现了11.3%的性能改进和13.9%的能源节约。当将我们的算法与其他三种著名的运行时映射算法进行比较时,就找到的映射的质量而言,它优于这些算法,同时与大多数这些算法相比,它还减少了开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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