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Runtime power allocation approach for GAMESS hybrid CPU-GPU implementation

机译:游戏混合CPU-GPU实现的运行时功率分配方法

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To improve power consumption of applications at the runtime, modern processors provide frequency scaling capabilities, which along with workload optimization, are also available on GPU accelerators. In this work, a runtime strategy is proposed to distribute a given power allocation among the host components and the GPU according to the current application performance and power usage, such that GPU execution is prioritized over CPU for power allocation to maximize application performance. Next, the strategy is tailored to an application, a quantum-chemistry package GAMESS for ab initio electronic structure calculations. Specifically, GAMESS hybrid CPU-GPU implementation as provided in the Libcchem library is considered. Experiments, performed on a 28-core node with a Kepler GPU, resulted in performance gains of up to 50% under the proposed strategy and the largest power allocation considered here as compared with the scenario when this allocation was equally distributed among the computing-platform components.
机译:为了提高运行时应用的应用功耗,现代处理器提供频率缩放功能,以及工作负载优化,也可以在GPU加速器上提供。在这项工作中,提出了一种运行时策略,以根据当前的应用性能和功率使用,提出了在主机组件和GPU之间分配给定功率分配,使得GPU执行通过CPU进行功率分配以最大化应用程序性能。接下来,该策略适用于应用,一种用于AB Initio电子结构计算的量子化学包装游戏。具体而言,考虑了Libcchem库中提供的游戏混合CPU-GPU实现。在具有开普勒GPU的28核节点上进行的实验导致在拟议的策略下的性能增益,并且在此分配在计算平台中同样分布时,这里考虑的最大功率分配组件。

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