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Graphics Processing Units (GPUs) have emerged as a promising platform for parallel computation. With a large number of scalar processors and abundant memory bandwidth, GPUs provide substantial computation power. While delivering high computation performance, the GPU also consumes high power and needs to be equipped with sufficient power supplies and cooling systems. Therefore, it is essential to institute an efficient mechanism for evaluating and understanding the power consumption requirement when running real applications on high-end GPUs. In this paper, we present a high-level GPU power consumption model using sophisticated tree-based random forest methods which can correlate the power consumption with a set of independent performance variables. This statistical model not only predicts the GPU runtime power consumption accurately, but more importantly, it also provides sufficient insights for understanding the dependence between the GPU runtime power consumption and the individual performance metrics. In order to gain more insights, we use a GPU simulator that can collect more runtime performance metrics than hardware counters. We measure the power consumption of a wide-range of CUDA kernels on an experimental system with GTX 280 GPU as statistical samples for our power analysis. This methodology can certainly be applied to any other CUDA GPU.