Scalable Simulation of 3D Wave Propagation in Semi-Infinite Domains Using the Finite Difference Method on a GPU Based Cluster
Resumo
The scattering of acoustic waves has been of practical interest for the petroleum industry, mainly in the determination of new oil deposits. A family of computational models that represent this phenomenon is based on finite difference methods. The simulation of these phenomena demands a high computational cost and large amounts of available memory. In this work we employ GPU based cluster environment for the development of scalable solvers for a 3D wave propagation problem with finite difference methods.
Referências
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Bolz, J., Farmer, I., Grinspun, E., and Schroder, P. (2003). Sparse matrix solvers on the gpu: conjugate gradients and multigrid. In ACM Transactions on Graphics: Proceedings of ACM SIGGRAPH, pages 917–924.
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Rozen, T., Boryczko, K., and Alda, W. (2008). A gpu-based method for approximate real-time fluid flow simulation. Machine Graphics and Vision International Journal, 17(3):267–278.
Zamith, M. P. M., Brandão, D. N., Kischinhevsky, M., Leal-Toledo, R. C. P., Filho, O. T. S., Montenegro, A. A., and Bulcão, A. (2010). Simulation of wave propagation in semi-infinite domains using the finite difference method on a gpu based on cluster. pages 7147–7157.
Balevic, A., Rockstroh, L., Tausendfreund, A., Patzelt, S., Goch, G., and Simon, S. (2008). Accelerating simulations of light scattering based on finite-difference time-domain method with general purpose gpus. Computational Science and Engineering, IEEE International Conference on, 0:327–334.
Bolz, J., Farmer, I., Grinspun, E., and Schroder, P. (2003). Sparse matrix solvers on the gpu: conjugate gradients and multigrid. In ACM Transactions on Graphics: Proceedings of ACM SIGGRAPH, pages 917–924.
Brandão, D., Zamith, M., Clua, E., Montenegro, A., Bulcão, A., Madeira, D., Kischinhevsky, M., and Leal-Toledo, R. (2010). Performance evaluation of optimized implementations of finite difference method for wave propagation problems on gpu architecture. In Computer Architecture and High Performance Computing Workshops (SBAC-PADW), 2010 22nd International Symposium on, pages 7–12.
Langdon, W. B. and W.Banzhaf (2008). A simd interpreter for genetic programming on gpu graphics cards. In Lecture Notes in Computer Science: Genetic Programming, pages 73–85. Springer Berlin-Heidelberg.
Michea, D. and D.Komatitsch (2010). Accelerating a three-dimensional finite-difference wave propagation code using gpu graphics cards. Geophysical Journal International, 182:389–402.
NVIDIA (2010). NVDIA - CUDA Programming Guide. NVIDIA.
Rozen, T., Boryczko, K., and Alda, W. (2008). A gpu-based method for approximate real-time fluid flow simulation. Machine Graphics and Vision International Journal, 17(3):267–278.
Zamith, M. P. M., Brandão, D. N., Kischinhevsky, M., Leal-Toledo, R. C. P., Filho, O. T. S., Montenegro, A. A., and Bulcão, A. (2010). Simulation of wave propagation in semi-infinite domains using the finite difference method on a gpu based on cluster. pages 7147–7157.
Publicado
19/07/2011
Como Citar
SABINO, Thales Luis Rodrigues et al.
Scalable Simulation of 3D Wave Propagation in Semi-Infinite Domains Using the Finite Difference Method on a GPU Based Cluster. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 5. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 989-996.
ISSN 2763-8774.
