Bounding energy consumption in large-scale MPI programs

B. Rountree, D. Lowenthal, S. Funk, V. Freeh, B. Supinski, M. Schulz
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引用次数: 177

Abstract

Power is now a first-order design constraint in large-scale parallel computing. Used carefully, dynamic voltage scaling can execute parts of a program at a slower CPU speed to achieve energy savings with a relatively small (possibly zero) time delay. However, the problem of when to change frequencies in order to optimize energy savings is NP-complete, which has led to many heuristic energy-saving algorithms. To determine how closely these algorithms approach optimal savings, we developed a system that determines a bound on the energy savings for an application. Our system uses a linear programming solver that takes as inputs the application communication trace and the cluster power characteristics and then outputs a schedule that realizes this bound. We apply our system to three scientific programs, two of which exhibit load imbalance---particle simulation and UMT2K. Results from our bounding technique show particle simulation is more amenable to energy savings than UMT2K.
大型MPI程序中的约束能耗
功率现在是大规模并行计算的一阶设计约束。仔细使用,动态电压缩放可以以较慢的CPU速度执行程序的某些部分,从而以相对较小(可能为零)的时间延迟实现节能。然而,何时改变频率以优化节能的问题是np完全的,这导致了许多启发式节能算法。为了确定这些算法与最优节能的接近程度,我们开发了一个系统来确定应用程序的节能界限。我们的系统采用线性规划求解器,将应用程序通信轨迹和集群功率特性作为输入,然后输出实现该边界的调度。我们将该系统应用于三个科学项目中,其中两个项目表现出负载不平衡——粒子模拟和UMT2K。我们的边界技术的结果表明,粒子模拟比UMT2K更适合节能。
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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