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High-Performance Reverse Time Migration on GPU

Javier Cabezas, Mauricio Araya-Polo, Isaac Gelado, Nacho Navarro, Enric Morancho, Jose M. Cela
Computer Sciences – Programming Models, Barcelona Supercomputing Center (BSC), Barcelona, Spain
International Conference of the Chilean Computer Science Society (SCCC), 2009

@inproceedings{cabezas2009high,

   title={High-performance reverse time migration on GPU},

   author={Cabezas, J. and Araya-Polo, M. and Gelado, I. and Navarro, N. and Morancho, E. and Cela, J.M.},

   booktitle={2009 International Conference of the Chilean Computer Science Society},

   pages={77–86},

   year={2009},

   organization={IEEE}

}

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Partial Differential Equations (PDE) are the heart of most simulations in many scientific fields, from Fluid Mechanics to Astrophysics. One the most popular mathematical schemes to solve a PDE is Finite Difference (FD). In this work we map a PDE-FD algorithm called Reverse Time Migration to a GPU using CUDA. This seismic imaging (Geophysics) algorithm is widely used in the oil industry. GPUs are natural contenders in the aftermath of the clock race, in particular for High-performance Computing (HPC). Due to GPU characteristics, the parallelism paradigm shifts from the classical threads plus SIMD to Single Program Multiple Data (SPMD). The NVIDIA GTX 280 implementation outperforms homogeneous CPUs up to 9x (Intel Harpertown E5420) and up to 14x (IBM PPC 970). These preliminary results confirm that GPUs are a real option for HPC, from performance to programmability.
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