基于硬件性能计数器的GPU功耗预测模型

A GPU Power Predication Model Based on Hardware Performance Counter

图形处理器GPU以其高性能、高能效优势成为当前异构高性能计算机系统主要采用的加速部件。虽然GPU具有较高的理论峰值能效,但其绝对功耗开销明显高于通用处理器。随着GPU在高性能计算领域的应用逐渐扩展,面向GPU的低功耗优化研究将成为该领域的重要研究方向之一。准确的功耗预测是功耗优化研究的重要前提,本文提出了基于硬件性能计数器的GPU功耗预测方法。该方法基于硬件性能计数器信息,结合GPU在部分运行频率下的功耗值,通过线性回归的方法预测处理器在其他运行频率下的功耗值。实验结果表明,该方法可以准确地预测GPU功耗。

Owing to its high performance and high power efficiency, GPU （Graphics Processing Units） has become the one of the most popular accelerators in heterogeneous high performance computing systems. Although GPU has relatively high peak power efficiency, the absolute power consumption is much higher than general-purpose CPUs. As GPUs being adopted by more high performance computing systems, the low-power optimization method specific to GPU will become one of the most hot topics in this field. Accurate power predication is an important basis for power optimization. This paper proposes a Hardware Performance Counter （HPC） based power predication method targeted for the GPU architecture. The method coordinates HPC and partial sample power consumption under specific running frequencies and makes use of the linear regression method to predicate the power consumption for other running frequencies. The experimental results validate the effectiveness of the proposed method.