Graphics Processing Units (GPUs) have emerged as a promising platform for parallel computation. With a large number of processor cores and abundant memory bandwidth, GPUs deliver substantial computation power. While providing high computation performance, a GPU consumes high power and needs sufficient power supplies and cooling systems. It is essential to institute an efficient mechanism for evaluating and understanding the power consumption 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 correlate and predict the power consumption using a set of performance variables. We demonstrate that this statistical model not only predicts the GPU runtime power consumption more accurately than existing regression based approaches, but more importantly, it provides sufficient insights into understanding the correlation of the GPU power consumption with individual performance metrics. 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 to collect statistical samples for power analysis. The proposed method is applicable to other GPUs as well.
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