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Measuring power and energy consumption of programs running on kepler GPUs

机译:测量在开普勒GPU上运行的程序的功耗和能耗

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Graphics processing units (GPUs) are becoming im-peccable choice for the upcoming exascale computing because of improvements in performance and power efficiency. In this paper, we propose an experimental methodology for evaluating power and energy consumption of programs executing on NVIDIA Kepler GPUs. NVIDIA Tesla K40c GPU is used in the experiments as a test platform. We applied our methodology on two commonly used high-performance computing (HPC) programs, Bitonic Mergesort (a parallel sorting program), and a matrix multiplication program. Using our methodology, power profile of any program executing on NVIDIA Kepler GPUs can be obtained to measure its peak power, average power, energy, and kernel runtime.
机译:由于性能和功率效率的提高,图形处理单元(GPU)成为即将到来的百亿分之一秒计算的理想选择。在本文中,我们提出了一种用于评估在NVIDIA Kepler GPU上执行的程序的功耗和能耗的实验方法。实验中使用NVIDIA Tesla K40c GPU作为测试平台。我们将方法论应用于两个常用的高性能计算(HPC)程序,Bitonic Mergesort(并行排序程序)和矩阵乘法程序。使用我们的方法,可以获得在NVIDIA Kepler GPU上执行的任何程序的功率曲线,以测量其峰值功率,平均功率,能量和内核运行时间。

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