Reliable and Energy-aware Mapping of Streaming Series-parallel Applications onto Hierarchical Platforms

Changjiang Gou, A. Benoit, Mingsong Chen, L. Marchal, Tongquan Wei
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Abstract

Streaming applications come from various application fields such as physics, and many can be represented as a series-parallel dependence graph. We aim at minimizing the energy consumption of such applications when executed on a hierarchical platform, by proposing novel mapping strategies. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it. In this paper, we propose a structure rule to partition the series-parallel applications, and we prove that the optimization problem is NP-complete. We are able to derive a dynamic programming algorithm for the special case of linear chains, which provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics performance is compared to a baseline solution, where each task is executed at maximum speed. Simulations demonstrate that significant energy savings can be obtained.
流串并联应用到分层平台的可靠和能量感知映射
流应用程序来自不同的应用领域,如物理,许多应用程序可以表示为串并联依赖图。通过提出新颖的映射策略,我们的目标是在分层平台上执行此类应用程序时最大限度地减少能耗。动态电压和频率缩放(DVFS)用于降低能耗,我们通过以最大速度执行任务或通过三倍执行任务来确保可靠的执行。本文提出了一个划分串并联应用的结构规则,并证明了该优化问题是np完全的。我们能够推导出线性链特殊情况下的动态规划算法,这为一般情况下的启发式设计提供了有趣的启发和基础。将启发式性能与基线解决方案进行比较,在基线解决方案中,每个任务都以最大速度执行。仿真结果表明,可以获得显著的节能效果。
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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