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GPUs has been widely used in scientific computing, as by offering exceptional performance as by power-efficient hardware. Its position established in high-performance and scientific computing communities has increased the urgency of understanding the power cost of GPU usage in accurate measurements. For this, the use of internal sensors are extremely important. In this work, we employ the GPU sensors to obtain high-resolution power profiles of real and benchmark applications. We wrote our own tools to query the sensors of two NVIDIA GPUs from different generations and compare the accuracy of them. Also, we compare the power profile of GPU with CPU using IPMItool.

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Acoustic waves are modeled using a 2D frequency-domain finite-difference scheme with three different artificial damping boundaries: the absorbing boundary condition (ABC), perfectly matched layer (PML), and a hybrid strategy combining PML and ABC. We evaluated the features of each simulation for a given artificial boundary condition for homogeneous and heterogeneous media. For shorter PML layers, the hybrid absorbing scheme gave better results when compared with the traditional PML technique, with a significant reduction of the spurious reflections and a shorter computational run time.