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.
Hardware accelerators such as GPGPUs and FPGAs have been used as an alternative to the conventional CPU in scientific computing applications and have shown significant performance improvements. In this context, this work presents an FPGA-based solution that explores efficiently the reuse of data and parallelization in both space and time domains for the first computational stage of the RTM (Reverse Time Migration) algorithm, the seismic modeling. We also implemented the same algorithm for CPU architectures and GPGPU and our results demonstrate that the FPGA-based approach can be a viable solution to improve performance. Experimental results show a speedup of 1.668 times compared with GPGPU and 25.79 times compared to CPU. Results were evaluated with the Marmousi velocity model, considering the same parameters in all approaches.
Simpósio em Sistemas Computacionais978-0-7695-4614-8/11 $26.00
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