Convergence analysis of run-time distributed optimization on adaptive systems using game theory

D. Puschini, F. Clermidy, P. Benoit, G. Sassatelli, L. Torres
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引用次数: 4

Abstract

We consider multiprocessor system-on-chip (MP-SoC) integrating several processing elements (PE). These architectures require distributed and scalable control techniques for run-time optimization of applicative parameters. Our approach is to use the game theory as an optimization model to solve the trade-off issues at run-time. We applied it to the distributed dynamic voltage frequency scaling (DVFS) management, adjusting at run-time the frequency set of each PE based on the synchronization between tasks of the application graph and the PE temperature profile. Results show that the analyzed algorithm converges to a solution in about 94% of the cases and in less than 40 calculation cycles for a 100-processor MP-SoC. It reaches an average optimization of 89% compared to an off-line centralized reference but about 140 times faster when simulating.
基于博弈论的自适应系统运行时分布式优化收敛性分析
我们考虑集成多个处理元件(PE)的多处理器片上系统(MP-SoC)。这些体系结构需要分布式和可扩展的控制技术来对应用程序参数进行运行时优化。我们的方法是使用博弈论作为优化模型来解决运行时的权衡问题。我们将其应用于分布式动态电压频率缩放(DVFS)管理,在运行时基于应用图任务与PE温度曲线之间的同步来调整每个PE的频率集。结果表明,对于100处理器的MP-SoC,所分析的算法在大约94%的情况下收敛到一个解决方案,并且在不到40个计算周期内。与离线集中式参考相比,它达到了89%的平均优化,但在模拟时快了约140倍。
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