Statistical power modeling of GPU kernels using performance counters

Abstract

We present a statistical approach for estimating power consumption of GPU kernels. We use the GPU performance counters that are exposed for CUDA applications, and train a linear regression model where performance counters are used as independent variables and power consumption is the dependent variable. For model training and evaluation, we use publicly available CUDA applications, consisting of 49 kernels in the CUDA SDK and the Rodinia benchmark suite. Our regression model achieves highly accurate estimates for many of the tested kernels, where the average error ratio is 4.7%. However, we also find that it fails to yield accurate estimates for kernels with texture reads because of the lack of performance counters for monitoring texture accesses, resulting in significant underestimation for such kernels.